WO2021056770A1 - 图像重建方法及装置、电子设备和存储介质 - Google Patents
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Definitions
- the present disclosure relates to the field of computer vision technology, and in particular to an image reconstruction method and device, electronic equipment and storage medium.
- Image reconstruction refers to the reconstruction of a noisy and fuzzy low-quality image into a clear and noise-free high-quality image, such as video image denoising, video super-division, or video deblurring. Different from the single image reconstruction task, how to effectively use the time information of the video (video frame information) is the key to reconstructing the video quality.
- the present disclosure proposes a technical solution for image processing.
- an image reconstruction method which includes:
- the acquiring the image feature corresponding to the first image in the video data and the image feature corresponding to the second image adjacent to the first image respectively includes:
- the feature optimization processing is performed on the image feature of the first image and the image feature of the second image to obtain the first optimized feature corresponding to the first image, and the The second optimization feature corresponding to the second image includes:
- Multi-frame information fusion processing is performed on the image feature of the first image and the image feature of the second image to obtain a first fusion feature corresponding to the first image and a second fusion feature corresponding to the second image, wherein, The first fusion feature is fused with the feature information of the second image, and the second fusion feature is fused with the feature information of the first image;
- the second optimization feature is obtained by processing.
- the multi-frame information fusion processing is performed on the image feature of the first image and the image feature of the second image to obtain the first fusion feature corresponding to the first image and the second image.
- the second fusion feature corresponding to the image includes:
- Two convolutional layers are used to perform convolution processing on the third optimized feature respectively to obtain the first fused feature and the second fused feature.
- the first fusion feature is used to perform single-frame optimization processing on the image feature of the first image to obtain the first optimized feature
- the second fusion feature is used to Performing single-frame optimization processing on the image feature of the second image to obtain the second optimized feature
- the second residual module is used to perform optimization processing on the first addition feature and the second addition feature, respectively, to obtain the first optimization feature and the second optimization feature.
- the performing feature fusion processing on the first optimized feature and the second optimized feature according to the correlation matrix between the first optimized feature and the second optimized feature to obtain the fused feature includes :
- the fusion feature is obtained based on the incidence matrix and the second connection feature.
- the acquiring the correlation matrix between the first optimized feature and the second optimized feature includes:
- the first optimization feature and the second optimization feature are input to a graph convolutional neural network, and the incidence matrix is obtained through the graph convolutional neural network.
- the obtaining the fusion feature based on the incidence matrix and the second connection feature includes:
- the activation function is used to activate the correlation matrix, and the product of the activated correlation matrix and the second connection feature is used to obtain the fusion feature.
- the performing image reconstruction processing on the first image by using the fusion feature to obtain a reconstructed image corresponding to the first image includes:
- a reconstructed image corresponding to the first image is obtained.
- the image reconstruction method is used to implement at least one of image desiccation processing, image super-division processing, and image deblurring processing.
- the image feature corresponding to the first image in the acquired video data and the first image adjacent to the first image are acquired.
- the image features corresponding to the two images respectively include:
- an image reconstruction device which includes:
- An acquiring module configured to acquire image features corresponding to a first image in the video data, and image features corresponding to second images adjacent to the first image;
- the optimization module is configured to perform feature optimization processing on the image feature of the first image and the image feature of the second image, and obtain the first optimized feature corresponding to the first image and the image feature corresponding to the second image respectively.
- the second optimization feature is configured to perform feature optimization processing on the image feature of the first image and the image feature of the second image, and obtain the first optimized feature corresponding to the first image and the image feature corresponding to the second image respectively.
- An association module configured to perform feature fusion processing on the first optimized feature and the second optimized feature according to the correlation matrix between the first optimized feature and the second optimized feature to obtain the fused feature
- the reconstruction module is configured to perform image reconstruction processing on the first image by using the fusion feature to obtain a reconstructed image corresponding to the image.
- the acquiring module is further configured to acquire at least one frame of second image directly adjacent and/or spaced adjacent to the first image;
- the optimization module includes:
- the multi-frame fusion unit is configured to perform multi-frame information fusion processing on the image feature of the first image and the image feature of the second image to obtain the first fusion feature corresponding to the first image and the image feature corresponding to the second image A second fusion feature, wherein the first fusion feature is fused with feature information of the second image, and the second fusion feature is fused with feature information of the first image;
- a single frame optimization unit configured to use the first fusion feature to perform a single frame optimization process on the image feature of the first image to obtain the first optimization feature, and to use the second fusion feature to perform a single frame optimization process on the second image Perform single-frame optimization processing on the image feature to obtain the second optimized feature.
- the multi-frame fusion unit is further configured to connect the image feature of the first image and the image feature of the second image to obtain the first connection feature;
- Two convolutional layers are used to perform convolution processing on the third optimized feature respectively to obtain the first fused feature and the second fused feature.
- the single frame optimization unit is further configured to perform addition processing on the image feature of the first image and the first fusion feature to obtain the first addition feature;
- the second residual module is used to perform optimization processing on the first addition feature and the second addition feature, respectively, to obtain the first optimization feature and the second optimization feature.
- the association module includes:
- An association unit configured to obtain an association matrix between the first optimized feature and the second optimized feature
- a connecting unit configured to connect the first optimization feature and the second optimization feature to obtain a second connection feature
- the fusion unit is configured to obtain the fusion feature based on the correlation matrix and the second connection feature.
- the correlation unit is further configured to input the first optimized feature and the second optimized feature into a graph convolutional neural network, and obtain the correlation matrix through the graph convolutional neural network .
- the fusion unit is further configured to use an activation function to activate the incidence matrix, and use the product of the activated incidence matrix and the second connection feature to obtain the Fusion features.
- the reconstruction unit is further configured to perform addition processing on the image feature of the first image and the fusion feature to obtain the image feature of the reconstructed image;
- a reconstructed image corresponding to the first image is obtained.
- the image reconstruction device is used to implement at least one of image desiccation processing, image super-division processing, and image deblurring processing.
- the acquisition module is further configured to perform up-sampling processing on the first image and the second image when the image reconstruction apparatus is used to implement image super-division processing;
- an electronic device including:
- a memory for storing processor executable instructions
- the processor is configured to call instructions stored in the memory to execute the method described in any one of the first aspect.
- a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method described in any one of the first aspect is implemented.
- a computer program including computer readable code, when the computer readable code runs in an electronic device, the processor in the electronic device executes The method described in any one of the first aspect is implemented.
- Fig. 1 shows a flowchart of an image reconstruction method according to an embodiment of the present disclosure
- Fig. 2 shows a flowchart of step S10 in an image reconstruction method according to an embodiment of the present disclosure
- Fig. 3 shows a flowchart of step S20 in an image reconstruction method according to an embodiment of the present disclosure
- Fig. 4 shows a flowchart of step S21 in an image reconstruction method according to an embodiment of the present disclosure
- Fig. 5 shows a flowchart of step S22 in an image reconstruction method according to an embodiment of the present disclosure
- Fig. 6 shows a flowchart of step S30 in an image reconstruction method according to an embodiment of the present disclosure
- FIG. 7 shows a schematic structural diagram of a neural network that implements an image reconstruction method according to an embodiment of the present disclosure
- Fig. 8 shows a block diagram of an image reconstruction device according to an embodiment of the present disclosure
- Fig. 9 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
- Fig. 10 shows a block diagram of another electronic device according to an embodiment of the present disclosure.
- the execution subject of the image reconstruction method in the embodiments of the present disclosure may be any image processing device.
- the image reconstruction method may be executed by a terminal device or a server or other processing device, where the terminal device may be a user equipment (UE) , Mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital assistants (PDAs), handheld devices, computing devices, in-vehicle devices, wearable devices, etc.
- the server may include a local server or a cloud server.
- the image reconstruction method may be implemented by a processor calling computer-readable instructions stored in the memory.
- the image reconstruction method of the embodiments of the present disclosure may be applied to perform image reconstruction processing on images in a video.
- the image reconstruction may include at least one of denoising, super-division, or deblurring processing on the image, which can improve the quality of the video image. Image Quality.
- Fig. 1 shows a flowchart of an image reconstruction method according to an embodiment of the present disclosure. As shown in Fig. 1, the image reconstruction method includes:
- the video data may be video information collected by any collection device, which may include at least two frames of images.
- the image to be reconstructed may be called the first image
- the image used to optimize the first image may be called the second image.
- the first image and the second image may be adjacent images.
- adjacent may include direct adjacent, or may also include spaced adjacent.
- the first image and the second image are directly adjacent to each other means that the first image and the second image are two images with a time frame difference of 1 in the video.
- the first image is the t-th frame image
- the second image can be t-1 Or t+1 frame image
- t is an integer greater than or equal to 1.
- the first image and the second image are adjacent to each other at an interval. It means that the first image and the second image are two images with a time frame difference greater than one in the video.
- the first image is the t-th frame image
- the second image is the t+a frame.
- Image, or ta frame image, a is an integer greater than 1.
- the second image may be one or multiple, which is not specifically limited in the present disclosure.
- the manner of determining the second image used to reconstruct the first image may determine the second image according to a preset rule, and the preset rule may include the number of second images and the comparison with the first image. The number of frames in the interval between an image, where the number of frames in the interval can be positive or negative.
- the image characteristics of the first image and the second image can be obtained.
- the pixel value corresponding to at least one pixel in the first image and the second image can be directly used as the image feature, or the first image and the second image can be obtained by performing feature extraction processing on the first image and the second image.
- the image characteristics of the image can be obtained.
- S20 Perform feature optimization processing on the image feature of the first image and the image feature of the second image to obtain a first optimized feature corresponding to the first image and a second optimized feature corresponding to the second image, respectively feature;
- the image features of the first image and the image features of the second image can be separately optimized by performing convolution processing to achieve the respective optimization of each image feature. Through this optimization, more detailed feature information can be added. , Improve the richness of features.
- the corresponding first optimized feature and the second optimized feature can be obtained respectively.
- the obtained features are respectively convolved through two convolutional layers, and the first optimized feature and the second optimized feature are obtained correspondingly.
- the correlation matrix between the first optimization feature and the second optimization feature can be further obtained, and the elements in the correlation matrix identify the first optimization.
- the correlation degree between the feature value at the same position in the feature and the second optimized feature is obtained.
- the obtained associated features may be used to perform feature fusion processing between the first optimized feature and the second optimized feature to obtain the fused feature.
- the image features of the second image and the image features in the first image can be effectively fused, which is beneficial to the reconstruction of the first image.
- S40 Perform image reconstruction processing on the first image by using the fusion feature to obtain a reconstructed image corresponding to the image.
- the fusion feature when the fusion feature is obtained, can be used to perform image reconstruction on the first image. For example, the fusion feature and the image feature of the first image can be added together to obtain the reconstructed image feature , The image corresponding to the reconstructed image feature is the reconstructed image.
- the embodiment of the present disclosure can obtain the correlation matrix obtained by the first optimization feature and the second optimization feature corresponding to the first image and the second image respectively, and the correlation matrix indicates that the first optimization feature and the second optimization feature are the same.
- the correlation between the feature information of the location, when the above-mentioned optimized feature fusion process is performed through the correlation matrix, the inter-frame information between the first image and the second image can be fused according to the correlation of different features at the same location, and then Improve the effect of reconstructed images.
- Fig. 2 shows a flowchart of step S10 in an image reconstruction method according to an embodiment of the present disclosure.
- said acquiring the image features corresponding to the first image in the video data and the image features respectively corresponding to the second image adjacent to the first image may include:
- S11 Acquire at least one frame of second image that is directly adjacent and/or spaced adjacent to the first image
- the first image to be reconstructed in the video data and at least one frame of the second image used to reconstruct the first image can be obtained, wherein the second image can be selected according to a preset rule, Or, at least one image can be randomly selected from the images adjacent to the first image as the second image, which is not specifically limited in the present disclosure.
- the preset rule may include the number of second images and the number of frames between the first image and the first image, and the corresponding second image can be determined by the number and number of frames.
- the preset rule may include that the number of the second image is 1, and the number of frames between the first image and the first image is +1, which means that the second image is a frame after the first image, for example, the first image If it is the t-th frame image, the second image is the t+1 frame image.
- S12 Perform feature extraction processing on the first image and the second image respectively to obtain image features corresponding to the first image and image features corresponding to the second image.
- the pixel values corresponding to the first image and the second image can be directly determined as image features, or feature extraction neural networks can be used to perform feature extraction processing on the first image and the second image respectively to obtain Corresponding image characteristics.
- Performing feature extraction processing through a feature extraction neural network can improve the accuracy of image features.
- the feature extraction neural network can be a convolutional neural network, such as a residual network, a feature pyramid network, or any other neural network that can achieve feature extraction.
- the present disclosure can also implement feature extraction processing through other methods. There is no specific restriction on this.
- feature optimization processing can be performed on the first image and the second image, and the first optimized features of the first image and the second image can be obtained correspondingly.
- the second optimization feature may separately perform feature optimization processing of the first image and the second image to obtain the corresponding first and second optimized features.
- the residual network can be used to process the image features of the first image and the image features of the second image respectively to obtain the first optimized feature of the first image and the second optimized feature of the second image.
- further convolution processing such as at least one layer of convolution processing
- Fig. 3 shows a flowchart of step S20 in an image reconstruction method according to an embodiment of the present disclosure.
- the feature optimization processing is performed on the image feature of the first image and the image feature of the second image to obtain the first optimized feature corresponding to the first image and the second optimized feature respectively.
- the second optimization feature corresponding to the image may include:
- S21 Perform multi-frame information fusion processing on the image feature of the first image and the image feature of the second image to obtain a first fusion feature corresponding to the first image and a second fusion feature corresponding to the second image.
- the first fusion feature is fused with feature information of the second image
- the second fusion feature is fused with feature information of the first image
- the first fusion feature corresponding to the first image and the second fusion feature corresponding to the second image can be obtained through the fusion of multiple frames of information between the image feature of the first image and the image feature of the second image. Fusion features.
- the image features of the first image and the second image can be fused with each other, so that the first fusion feature and the second fusion feature both include the feature information of the first image and the second image, respectively.
- S22 Use the first fusion feature to perform single-frame optimization processing on the image feature of the first image to obtain the first optimized feature, and use the second fusion feature to perform single-frame optimization on the image feature of the second image.
- the frame optimization process obtains the second optimization feature.
- the first fusion feature of the first image and the second fusion feature of the second image when the first fusion feature of the first image and the second fusion feature of the second image are obtained, the first fusion feature can be used to perform the feature of a single frame image on the image feature of the first image. Fusion (that is, single-frame optimization processing), and using the second fusion feature to perform feature fusion of the single-frame image on the image feature of the second image, and correspondingly obtain the first optimized feature and the second optimized feature.
- the single-frame optimization process can further strengthen the respective image features on the basis of the first fusion feature and the second fusion feature, so that the obtained first optimization feature is also fused at the same time on the basis of the image features of the first image.
- the feature information of the second image and the obtained second optimized feature are simultaneously fused with the feature information of the first image on the basis of the image feature of the second image.
- the above-mentioned optimization process may be performed at least once, that is, at least one multi-frame information fusion and single-frame optimization process may be performed.
- the first optimization process can directly use the image features of the first image and the second image as the object of the optimization process.
- the object of the n+1th optimization process is the nth optimization process.
- Process the optimized features of the output that is to say, you can continue to perform multi-frame information fusion and single-frame optimization processing on the two optimized features obtained by the nth optimization process to obtain the final optimized features (first optimized feature and second optimized feature) .
- the accuracy of the obtained feature information and the richness of features can be further improved.
- Fig. 4 shows a flowchart of step S21 in an image reconstruction method according to an embodiment of the present disclosure.
- the multi-frame information fusion processing is performed on the image feature of the first image and the image feature of the second image to obtain the first fusion feature corresponding to the first image and the second image corresponding
- the second fusion feature of can include:
- the image feature of the first image and the image feature of the second image may be connected first, for example, in the channel direction to obtain the first connection feature .
- the concat function connection function
- the concat function can be used to connect the image feature of the first image and the image feature of the second image, so that the two frames of image information can be simply fused.
- the first connection feature when the first connection feature is obtained, the first connection feature may be further optimized.
- a residual network can be used to perform the feature optimization processing.
- the first connection feature can be input to the first residual block (residual block) to perform feature optimization to obtain the third optimized feature.
- the feature information in the first connection feature can be further fused and the accuracy of the feature information can be improved, that is, the feature information in the first image and the second image is further accurately fused in the third optimized feature .
- S213 Perform convolution processing on the third optimized feature by using two convolution layers to obtain the first fusion feature and the second fusion feature.
- different convolution layers may be used to perform convolution processing on the third optimized feature.
- two convolutional layers may be used to perform convolution processing on the third optimized feature, respectively, to obtain the first fusion feature and the second fusion feature respectively.
- the two convolutional layers can be, but are not limited to, a 1*1 convolution kernel.
- the first fusion feature includes the feature information of the second image
- the second fusion feature also includes the feature information of the first image, that is, both the first fusion feature and the second fusion feature include the feature information of the two images each other .
- Fig. 5 shows a flowchart of step S22 in an image reconstruction method according to an embodiment of the present disclosure.
- the use of the first fusion feature to perform single-frame optimization processing on the image feature of the first image to obtain the first optimized feature, and the use of the second fusion feature to perform single-frame optimization on the image feature of the second image includes:
- S221 Perform addition processing on the image feature of the first image and the first fusion feature to obtain a first addition feature, and perform addition processing on the image feature of the second image and the second fusion feature to obtain the first Two plus features;
- the first fusion feature when the first fusion feature is obtained, can be used to perform the optimization processing of the single frame information of the first image.
- the embodiment of the present disclosure can use the image feature of the first image and the first image.
- the optimization process is performed in a fusion feature summation method.
- the summation may include the direct addition of the first fusion feature and the image feature of the first image, or may include the weighted phase of the first fusion feature and the image feature of the first image. Adding, that is, the first fusion feature and the image feature of the first image are respectively multiplied by the corresponding weighting coefficients and then performing an addition operation.
- the weighting coefficients can be preset values or values learned by neural networks. The present disclosure There is no specific restriction on this.
- the second fusion feature can be used to perform the optimization processing of the single frame information of the second image, and the embodiment of the present disclosure can use the image feature of the second image and the second fusion feature to add
- the optimization process is performed in the manner of, and the addition may include the direct addition of the second fusion feature and the image feature of the second image, or it may include the weighted addition of the second fusion feature and the image feature of the second image, that is, the second The fusion feature and the image feature of the second image are respectively multiplied by the corresponding weighting coefficient and then added and calculated.
- the weighting coefficient can be a preset value or a value learned by a neural network, which is not specifically limited in the present disclosure. .
- the time for the embodiment of the present disclosure to perform the addition processing on the image feature of the first image and the first fusion feature, and the time for performing the addition processing on the image feature of the second image and the second fusion feature is different. To make specific restrictions, the two can be executed separately or simultaneously.
- the feature information of the original image can be further increased on the basis of the fusion feature.
- the optimization of a single frame of information can realize that the characteristic information of a single frame of image can be retained at each stage of the network, and then the single frame of information can be optimized according to the optimized multi-frame information.
- the embodiments of the present disclosure may directly use the above-mentioned first addition feature and second addition feature as the first optimization feature and the second optimization feature, or perform subsequent optimization processing to further improve the accuracy of the feature.
- S222 Use the second residual module to perform optimization processing on the first addition feature and the second addition feature, respectively, to obtain the first optimization feature and the second optimization feature.
- optimization processing may be further performed on the first addition feature and the second addition feature, for example, the first addition feature and the second addition feature can be optimized separately.
- the sum feature and the second addition feature perform convolution processing to obtain the first optimized feature and the second optimized feature.
- the embodiments of the present disclosure respectively perform optimization processing of the first addition feature and the second addition feature through the residual network.
- the residual network here is called the second residual. Module.
- the second residual module performs encoding convolution and decoding convolution on the first addition feature and the second addition feature, respectively, to achieve further optimization and optimization of the feature information in the first addition feature and the second addition feature. Fusion, respectively obtain the first optimization feature corresponding to the first addition feature and the second optimization feature corresponding to the second addition feature.
- the fusion of multiple frames of information in the first image and the second image and the optimization of single frame information can be realized.
- the features of the remaining images can also be fused Information, through the fusion of information between frames, improve the accuracy of the reconstructed image.
- Fig. 6 shows a flowchart of step S30 in an image reconstruction method according to an embodiment of the present disclosure.
- the performing feature fusion processing on the first optimized feature and the second optimized feature according to the correlation matrix between the first optimized feature and the second optimized feature to obtain the fused feature includes:
- the correlation matrix between the first optimized feature and the second optimized feature may be further obtained
- the correlation matrix may indicate the degree of correlation between the feature information corresponding to the same position in the first optimized feature and the second optimized feature.
- the degree of association can reflect the changes in the first image and the second image for the same object or person object.
- the scales of the first image and the second image may be the same, and the scales of the corresponding first optimized feature and the second optimized feature are also the same.
- the aforementioned corresponding features can also be adjusted to the same scale, for example, the scale adjustment operation is performed through pooling processing.
- the embodiment of the present disclosure can obtain the correlation matrix between the first optimized feature and the second optimized feature through the graph convolutional neural network, that is, the first optimized feature and the second optimized feature can be input into the graph convolutional neural network,
- the graph convolutional neural network is used to perform processing on the first optimized feature and the second optimized feature, and the correlation matrix between the two is obtained.
- the first optimization feature and the second optimization feature may be connected, for example, the first optimization feature and the second optimization feature are connected in the channel direction. 2. Optimization features.
- the connection process can be executed through the concat function to obtain the second connection feature.
- embodiments of the present disclosure may not limit the execution steps of steps S31 and S32, and the two steps may be executed simultaneously or separately.
- an activation function can be used to perform processing on the incidence matrix.
- the activation function can be a softmax function, in which the degree of association in the incidence matrix can be used as input Parameters, and then use the activation function to perform processing on at least one input parameter, and output the processed incidence matrix.
- the embodiment of the present disclosure may use the product of the correlation matrix after activation function activation processing and the second connection feature to obtain the fusion feature.
- the fusion of feature information at the same position of multiple frames of images can be performed through an incidence matrix.
- the fusion feature can be further used to perform the reconstruction processing of the first image, wherein the image feature and the fusion feature of the first image can be added together to obtain the image feature corresponding to the reconstructed image, and then The reconstructed image can be determined according to the image characteristics of the reconstructed image.
- the addition processing may be direct addition, or weighted addition using weighting coefficients, which is not specifically limited in the present disclosure.
- the image feature of the reconstructed image can directly correspond to the pixel value of at least one pixel of the reconstructed image, so the image feature of the reconstructed image can be directly used to obtain the reconstructed image.
- the image reconstruction method can be used to achieve at least one of image denoising, super-division, and deblurring, and image quality can be improved to varying degrees through image reconstruction.
- acquiring the image feature corresponding to the first image in the video data and the image feature corresponding to the second image adjacent to the first image may include:
- the up-sampling process may be performed on the first image and the second image first, for example, the up-sampling process may be performed through at least one convolution process, or interpolation fitting may be performed. Upsampling is performed in the same way. Through the up-sampling process, the feature information in the image can be further enriched.
- the image reconstruction method of the embodiment of the present disclosure can be used to perform feature optimization processing on the up-sampled first image and the second image, as well as subsequent feature fusion and processing. Image reconstruction processing. Through the above configuration, the image accuracy of the reconstructed image can be further improved.
- the first optimized feature corresponding to the first image and the second optimized feature corresponding to the second image can be obtained through optimization processing of the image feature of the first image and the image feature of the second image in the video data
- the correlation matrix between the first optimized feature and the second optimized feature is used to perform feature fusion between the first optimized feature and the second optimized feature
- the obtained fused feature is used to reconstruct the first image to obtain a reconstructed image.
- the correlation matrix obtained through the first optimized feature and the second optimized feature can represent the correlation between the feature information at the same position in the first optimized feature and the second optimized feature.
- the process of reconstructing the image in the video in the embodiments of the present disclosure may include the following processes:
- Multi-frame information fusion path (mixing path). First, use the concat method to simply fuse multiple frames of information, and then after the convolutional layer is optimized, it is transformed into a single frame of information for spatial output.
- Fig. 7 shows a schematic structural diagram of a neural network implementing an image reconstruction method according to an embodiment of the present disclosure. Among them, as shown in FIG. 7, the t-th frame image and the t+1-th frame image in the video data are first obtained. Among them, the network part A in the neural network is used to implement feature optimization processing of image features, and the network part B is used to implement feature fusion processing and image reconstruction processing.
- the input of the neural network can be the feature information (image feature) F1 of the t frame and the feature information (image feature) F2 of the t+1 frame, or it can be directly the t-th frame image and the t+1-th frame image;
- Single-frame information optimization path (self-refining path). In each stage of the network, the characteristic information of a single frame is retained, and then the single frame information is optimized according to the optimized multi-frame information.
- the information (image feature) of the previous stage t frame and the corresponding optimized fusion information (first fusion feature) are added, and then optimized through the residual block (residual block) to obtain The first optimization feature F3.
- the same processing procedure is performed for the t+1 frame, and the second optimized feature F4 is obtained.
- Pixel association module In the last stage of the whole model (Part B), the pixel correlation module is used to calculate the correlation matrix between multiple frames, and then the multi-frame information is merged according to the correlation matrix.
- the concatenation connection result (the second connection feature) of the two frames of feature information (the first optimization feature and the second optimization feature) is input into a 1d convolutional layer (one-dimensional convolutional layer) to calculate an incidence matrix. Then the association matrix is subjected to a softmax operation and multiplied by the concatenation result of the two frames of feature information to obtain the optimized information (fusion feature) F5 of the two frames.
- a skip connection is used to add the current frame t frame input from the network and the optimized feature information to obtain the final reconstructed image.
- the fusion feature F5 and the image feature F1 of the t frame image can be added together to obtain the image feature F of the reconstructed image, and then the reconstructed image can be directly correspondingly obtained.
- the first optimized feature corresponding to the first image and the second optimized feature corresponding to the first image can be obtained through optimization processing of the image feature of the first image and the image feature of the second image in the video data.
- the second optimization feature and using the correlation matrix between the first optimization feature and the second optimization feature, perform feature fusion between the first optimization feature and the second optimization feature, and use the obtained fusion feature to reconstruct the first image. Reconstruct the image.
- the correlation matrix obtained through the first optimized feature and the second optimized feature can represent the correlation between the feature information at the same position in the first optimized feature and the second optimized feature.
- the inter-frame information can be fused according to the correlation of different features at the same position, and the reconstructed image effect obtained is better.
- the embodiments of the present disclosure not only effectively retain the information of a single frame, but also make full use of the inter-frame information merged multiple times.
- the embodiments of the present disclosure can optimize the inter-frame information by using the correlation of the inter-frame information based on the method of graph convolution, and further improve the feature accuracy.
- the writing order of the steps does not mean a strict execution order but constitutes any limitation on the implementation process.
- the specific execution order of each step should be based on its function and possibility.
- the inner logic is determined.
- the present disclosure also provides image reconstruction devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the image reconstruction methods provided in the present disclosure.
- image reconstruction devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the image reconstruction methods provided in the present disclosure.
- Fig. 8 shows a block diagram of an image reconstruction device according to an embodiment of the present disclosure. As shown in Fig. 8, the image reconstruction device includes:
- the acquiring module 10 is configured to acquire image features corresponding to a first image in the video data, and image features corresponding to second images adjacent to the first image, respectively;
- the optimization module 20 is configured to perform feature optimization processing on the image features of the first image and the image features of the second image to obtain first optimized features corresponding to the first image and corresponding to the second image respectively The second optimization feature;
- the correlation module 30 is configured to perform feature fusion processing on the first optimized feature and the second optimized feature according to the correlation matrix between the first optimized feature and the second optimized feature to obtain the fused feature;
- the reconstruction module 40 is configured to perform image reconstruction processing on the first image by using the fusion feature to obtain a reconstructed image corresponding to the image.
- the acquiring module is further configured to acquire at least one frame of second image directly adjacent and/or spaced adjacent to the first image;
- the optimization module includes:
- the multi-frame fusion unit is configured to perform multi-frame information fusion processing on the image feature of the first image and the image feature of the second image to obtain the first fusion feature corresponding to the first image and the image feature corresponding to the second image A second fusion feature, wherein the first fusion feature is fused with feature information of the second image, and the second fusion feature is fused with feature information of the first image;
- a single frame optimization unit configured to use the first fusion feature to perform a single frame optimization process on the image feature of the first image to obtain the first optimization feature, and to use the second fusion feature to perform a single frame optimization process on the second image Perform single-frame optimization processing on the image feature to obtain the second optimized feature.
- the multi-frame fusion unit is also used to connect the image feature of the first image and the image feature of the second image to obtain the first connection feature;
- Two convolutional layers are used to perform convolution processing on the third optimized feature respectively to obtain the first fused feature and the second fused feature.
- the single frame optimization unit is further configured to perform addition processing on the image feature of the first image and the first fusion feature to obtain the first addition feature;
- the second residual module is used to perform optimization processing on the first addition feature and the second addition feature, respectively, to obtain the first optimization feature and the second optimization feature.
- the association module includes:
- An association unit configured to obtain an association matrix between the first optimized feature and the second optimized feature
- a connecting unit configured to connect the first optimization feature and the second optimization feature to obtain a second connection feature
- the fusion unit is configured to obtain the fusion feature based on the correlation matrix and the second connection feature.
- the correlation unit is further configured to input the first optimized feature and the second optimized feature into a graph convolutional neural network, and obtain the correlation matrix through the graph convolutional neural network .
- an activation function is used to activate the correlation matrix, and the product of the correlation matrix after the activation processing and the second connection feature is used to obtain the fusion feature.
- the construction unit is further configured to perform addition processing on the image feature of the first image and the fusion feature to obtain the image feature of the reconstructed image;
- a reconstructed image corresponding to the first image is obtained.
- the image reconstruction device is used to implement at least one of image desiccation processing, image super-division processing, and image deblurring processing.
- the acquisition module is further configured to perform up-sampling processing on the first image and the second image when the image reconstruction apparatus is used to implement image super-division processing;
- the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
- the functions or modules contained in the device provided in the embodiments of the present disclosure can be used to execute the methods described in the above method embodiments.
- the embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
- the computer-readable storage medium may be a non-volatile computer-readable storage medium.
- An embodiment of the present disclosure also provides an electronic device, including: a processor; a memory for storing executable instructions of the processor; wherein the processor is configured as the above method.
- the electronic device can be provided as a terminal, server or other form of device.
- Fig. 9 shows a block diagram of an electronic device according to an embodiment of the present disclosure.
- the electronic device 800 may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, and other terminals.
- the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, and a sensor component 814 , And communication component 816.
- the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
- the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
- the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
- the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
- the memory 804 is configured to store various types of data to support operations in the electronic device 800. Examples of these data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc.
- the memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.
- SRAM static random access memory
- EEPROM electrically erasable programmable read-only memory
- EPROM erasable and Programmable read only memory
- PROM programmable read only memory
- ROM read only memory
- magnetic memory flash memory
- flash memory magnetic disk or optical disk.
- the power supply component 806 provides power for various components of the electronic device 800.
- the power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
- the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
- the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
- the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
- the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
- the audio component 810 is configured to output and/or input audio signals.
- the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
- the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
- the audio component 810 further includes a speaker for outputting audio signals.
- the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
- the peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: home button, volume button, start button, and lock button.
- the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
- the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
- the component is the display and the keypad of the electronic device 800.
- the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
- the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
- the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
- the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
- the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
- the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
- the electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
- the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
- the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
- the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
- RFID radio frequency identification
- IrDA infrared data association
- UWB ultra-wideband
- Bluetooth Bluetooth
- the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
- ASIC application-specific integrated circuits
- DSP digital signal processors
- DSPD digital signal processing devices
- PLD programmable logic devices
- FPGA field-available A programmable gate array
- controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
- a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.
- Fig. 10 shows a block diagram of another electronic device according to an embodiment of the present disclosure.
- the electronic device 1900 may be provided as a server.
- the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932, for storing instructions executable by the processing component 1922, such as application programs.
- the application program stored in the memory 1932 may include one or more modules each corresponding to a set of instructions.
- the processing component 1922 is configured to execute instructions to perform the above-described methods.
- the electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to the network, and an input output (I/O) interface 1958 .
- the electronic device 1900 can operate based on an operating system stored in the memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.
- a non-volatile computer-readable storage medium is also provided, such as the memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to complete the foregoing method.
- the present disclosure may be a system, method and/or computer program product.
- the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
- the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
- the computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
- Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
- RAM random access memory
- ROM read-only memory
- EPROM erasable programmable read-only memory
- flash memory flash memory
- SRAM static random access memory
- CD-ROM compact disk read-only memory
- DVD digital versatile disk
- memory stick floppy disk
- mechanical encoding device such as a printer with instructions stored thereon
- the computer-readable storage medium used here is not interpreted as the instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
- the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
- the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
- the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
- the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages.
- Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
- Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out.
- the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access connection).
- LAN local area network
- WAN wide area network
- an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
- the computer-readable program instructions are executed to realize various aspects of the present disclosure.
- These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner. Thus, the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
- each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
- Executable instructions may also occur in a different order than the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
- each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
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Abstract
Description
Claims (25)
- 一种图像重建方法,包括:获取视频数据中的第一图像对应的图像特征,以及与所述第一图像相邻的第二图像分别对应的图像特征;对所述第一图像的图像特征和第二图像的图像特征执行特征优化处理,分别得到与所述第一图像对应的第一优化特征,以及与所述第二图像对应的第二优化特征;根据所述第一优化特征和第二优化特征之间的关联矩阵,对所述第一优化特征和第二优化特征执行特征融合处理,得到融合特征;利用所述融合特征对所述第一图像执行图像重建处理,得到所述图像对应的重建图像。
- 根据权利要求1所述的方法,其特征在于,所述获取视频数据中的第一图像对应的图像特征以及与所述第一图像相邻的第二图像分别对应的图像特征,包括:获取与所述第一图像直接相邻和/或间隔相邻的至少一帧第二图像;分别对所述第一图像和所述第二图像执行特征提取处理,得到所述第一图像对应的图像特征以及所述第二图像对应的图像特征。
- 根据权利要求1或2所述的方法,其特征在于,所述对所述第一图像的图像特征和第二图像的图像特征执行特征优化处理,分别得到与所述第一图像对应的第一优化特征,以及与所述第二图像对应的第二优化特征,包括:对所述第一图像的图像特征和第二图像的图像特征执行多帧信息融合处理,得到所述第一图像对应的第一融合特征以及所述第二图像对应的第二融合特征,其中,所述第一融合特征融合有所述第二图像的特征信息,所述第二融合特征融合有所述第一图像的特征信息;利用所述第一融合特征对所述第一图像的图像特征执行单帧优化处理得到所述第一优化特征,以及利用所述第二融合特征对所述第二图像的图像特征执行单帧优化处理得到所述第二优化特征。
- 根据权利要求3所述的方法,其特征在于,所述对所述第一图像的图像特征和第二图像的图像特征执行多帧信息融合处理,得到所述第一图像对应的第一融合特征以及所述第二图像对应的第二融合特征,包括:连接所述第一图像的图像特征和所述第二图像的图像特征,得到第一连接特征;利用第一残差模块对所述第一连接特征执行优化处理,得到第三优化特征;利用两个卷积层分别对所述第三优化特征执行卷积处理,得到所述第一融合特征和第二融合特征。
- 根据权利要求3或4所述的方法,其特征在于,所述利用所述第一融合特征对所述第一图像的图像特征执行单帧优化处理得到所述第一优化特征,以及利用所述第二融合特征对所述第二图像的图像特征执行单帧优化处理得到所述第二优化特征,包括:对所述第一图像的图像特征与第一融合特征执行加和处理,得到第一加和特征;对所述第二图像的图像特征与第二融合特征执行加和处理,得到第二加和特征;利用第二残差模块分别对所述第一加和特征和所述第二加和特征执行优化处理,得到所述第一优化特征和第二优化特征。
- 根据权利要求1-5中任意一项所述的方法,其特征在于,所述根据所述第一优化特征和第二优化特征之间的关联矩阵,对所述第一优化特征和第二优化特征执行特征融合处理,得到融合特征,包括:获取所述第一优化特征和第二优化特征之间的关联矩阵;对所述第一优化特征和第二优化特征进行连接,得到第二连接特征;基于所述关联矩阵和所述第二连接特征得到所述融合特征。
- 根据权利要求6所述的方法,其特征在于,所述获取所述第一优化特征和第二优 化特征之间的关联矩阵,包括:将所述第一优化特征和所述第二优化特征输入到图卷积神经网络,通过所述图卷积神经网络得到所述关联矩阵。
- 根据权利要求6或7所述的方法,其特征在于,所述基于所述关联矩阵和所述第二连接特征得到所述融合特征,包括:利用激活函数对所述关联矩阵进行激活处理,并利用激活处理后的关联矩阵与所述第二连接特征之间的乘积,得到所述融合特征。
- 根据权利要求1-8中任意一项所述的方法,其特征在于,所述利用所述融合特征对所述第一图像执行图像重建处理,得到所述第一图像对应的重建图像,包括:对所述第一图像的图像特征和所述融合特征执行加和处理,得到所述重建图像的图像特征;利用所述重建图像的图像特征,得到所述第一图像对应的重建图像。
- 根据权利要求1-9中任意一项所述的方法,其特征在于,所述图像重建方法用于实现图像去燥处理、图像超分处理以及图像去模糊处理中的至少一种。
- 根据权利要求10所述的方法,其特征在于,在所述图像重建方法用于实现图像超分处理的情况下,所述获取视频数据中的第一图像对应的图像特征以及与所述第一图像相邻的第二图像分别对应的图像特征,包括:对所述第一图像和所述第二图像执行上采样处理;对上采样处理后的所述第一图像和第二图像执行特征提取处理,得到所述第一图像对应的图像特征以及所述第二图像对应的图像特征。
- 一种图像重建装置,包括:获取模块,用于获取视频数据中的第一图像对应的图像特征,以及与所述第一图像相邻的第二图像分别对应的图像特征;优化模块,用于对所述第一图像的图像特征和第二图像的图像特征执行特征优化处理,分别得到与所述第一图像对应的第一优化特征,以及与所述第二图像对应的第二优化特征;关联模块,用于根据所述第一优化特征和第二优化特征之间的关联矩阵,对所述第一优化特征和第二优化特征执行特征融合处理,得到融合特征;重建模块,用于利用所述融合特征对所述第一图像执行图像重建处理,得到所述图像对应的重建图像。
- 根据权利要求12所述的装置,其特征在于,所述获取模块还用于获取与所述第一图像直接相邻和/或间隔相邻的至少一帧第二图像;分别对所述第一图像和所述第二图像执行特征提取处理,得到所述第一图像对应的图像特征以及所述第二图像对应的图像特征。
- 根据权利要求12或13所述的装置,其特征在于,所述优化模块包括:多帧融合单元,用于对所述第一图像的图像特征和第二图像的图像特征执行多帧信息融合处理,得到所述第一图像对应的第一融合特征以及所述第二图像对应的第二融合特征,其中,所述第一融合特征融合有所述第二图像的特征信息,所述第二融合特征融合有所述第一图像的特征信息;单帧优化单元,用于利用所述第一融合特征对所述第一图像的图像特征执行单帧优化处理得到所述第一优化特征,以及利用所述第二融合特征对所述第二图像的图像特征执行单帧优化处理得到所述第二优化特征。
- 根据权利要求14所述的装置,其特征在于,多帧融合单元还用于连接所述第一图像的图像特征和所述第二图像的图像特征,得到第一连接特征;利用第一残差模块对所述第一连接特征执行优化处理,得到第三优化特征;利用两个卷积层分别对所述第三优化特征执行卷积处理,得到所述第一融合特征和第二融合特征。
- 根据权利要求14或15所述的装置,其特征在于,所述单帧优化单元还用于对所述第一图像的图像特征与第一融合特征执行加和处理,得到第一加和特征;对所述第二图像的图像特征与第二融合特征执行加和处理,得到第二加和特征;利用第二残差模块分别对所述第一加和特征和所述第二加和特征执行优化处理,得到所述第一优化特征和第二优化特征。
- 根据权利要求12-16中任意一项所述的装置,其特征在于,所述关联模块包括:关联单元,用于获取所述第一优化特征和第二优化特征之间的关联矩阵;连接单元,用于对所述第一优化特征和第二优化特征进行连接,得到第二连接特征;融合单元,用于基于所述关联矩阵和所述第二连接特征得到所述融合特征。
- 根据权利要求17所述的装置,其特征在于,所述关联单元还用于将所述第一优化特征和所述第二优化特征输入到图卷积神经网络,通过所述图卷积神经网络得到所述关联矩阵。
- 根据权利要求17或18所述的装置,其特征在于,所述融合单元还用于利用激活函数对所述关联矩阵进行激活处理,并利用激活处理后的关联矩阵与所述第二连接特征之间的乘积,得到所述融合特征。
- 根据权利要求12-19中任意一项所述的装置,其特征在于,所述重建单元还用于对所述第一图像的图像特征和所述融合特征执行加和处理,得到所述重建图像的图像特征;利用所述重建图像的图像特征,得到所述第一图像对应的重建图像。
- 根据权利要求12-20中任意一项所述的装置,其特征在于,所述图像重建装置用于实现图像去燥处理、图像超分处理以及图像去模糊处理中的至少一种。
- 根据权利要求21所述的装置,其特征在于,所述获取模块还用于在所述图像重建装置用于实现图像超分处理的情况下,对所述第一图像和所述第二图像执行上采样处理;对上采样处理后的所述第一图像和第二图像执行特征提取处理,得到所述第一图像对应的图像特征以及所述第二图像对应的图像特征。
- 一种电子设备,包括:处理器;用于存储处理器可执行指令的存储器;其中,所述处理器被配置为调用所述存储器存储的指令,以执行权利要求1至11中任意一项所述的方法。
- 一种计算机可读存储介质,其上存储有计算机程序指令,所述计算机程序指令被处理器执行时实现权利要求1至11中任意一项所述的方法。
- 一种计算机程序,所述计算机程序包括计算机可读代码,当所述计算机可读代码在电子设备中运行时,所述电子设备中的处理器执行用于实现权利要求1至11中的任意一项所述的方法。
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