CN109003231B - Image enhancement method and device and display equipment - Google Patents
Image enhancement method and device and display equipment Download PDFInfo
- Publication number
- CN109003231B CN109003231B CN201810596553.6A CN201810596553A CN109003231B CN 109003231 B CN109003231 B CN 109003231B CN 201810596553 A CN201810596553 A CN 201810596553A CN 109003231 B CN109003231 B CN 109003231B
- Authority
- CN
- China
- Prior art keywords
- image
- network model
- neural network
- layer corresponding
- enhanced
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000005286 illumination Methods 0.000 claims abstract description 59
- 238000003062 neural network model Methods 0.000 claims abstract description 50
- 238000013527 convolutional neural network Methods 0.000 claims description 36
- 238000012549 training Methods 0.000 claims description 35
- 238000001914 filtration Methods 0.000 claims description 23
- 238000012545 processing Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 9
- 238000010276 construction Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The application provides an image enhancement method and device. The image enhancement method provided by the application comprises the following steps: constructing an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image; acquiring an illumination layer corresponding to an image to be processed; and inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model, and outputting the enhanced image. According to the image enhancement method and device, the constructed image enhancement neural network model can remove illumination changes in the image to be processed according to the input illumination image layer, and the image enhancement effect is improved.
Description
Technical Field
The present application relates to image processing technologies, and in particular, to an image enhancement method and apparatus, and a display device.
Background
At present, in the field of education, images are usually shot by a camera for blackboard writing, and the shot images are used as notes for teachers to give lessons, so that the teachers can conveniently look up the images and can help students to remember and understand the images. However, the image may be affected by the blackboard material, the intensity of the ambient light, and other factors in the process of obtaining the image, which may cause the image to have the phenomena of low contrast, unobvious image information, color distortion, or insufficient definition of the boundary information, and affect the reading of the characters in the image. Therefore, enhancement processing of the image is required.
With the development of deep learning technology, convolutional neural networks have been widely applied in the field of image enhancement. Specifically, an enhanced image is obtained by convolving an input low-quality image with a series of convolution kernels through an end-to-end network. However, when the convolutional neural network is used to enhance the image of the photographed blackboard image, the convolution kernel cannot capture the illumination change in the image, and the image enhancement effect is poor.
Disclosure of Invention
In view of the above, the present application provides an image enhancement method, an image enhancement device, and a display device, so as to solve the problem that the effect is poor when the existing method is used to enhance a blackboard image.
A first aspect of the present application provides an image enhancement method, including:
constructing an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image;
acquiring an illumination layer corresponding to an image to be processed;
and inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model, and outputting the enhanced image.
Further, the constructing the image-enhanced neural network model specifically includes:
constructing a convolutional neural network model, wherein the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image;
constructing a training set, wherein the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image;
and training the convolutional neural network model by using the training set to obtain the image enhancement neural network model.
Further, the illumination layer corresponding to the image is obtained by the following method:
converting the image from a first color space to a second color space containing first luminance information, and extracting the first luminance information of the image;
filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image;
calculating an average value of second luminance information of all pixels of the gray-scale image;
and correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
Further, the filtering algorithm is a median filtering algorithm.
Further, the convolutional neural network model is a full convolutional network model.
A second aspect of the present application provides an image enhancement apparatus comprising: a construction module, an acquisition module and a processing module, wherein,
the building module is used for building an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image;
the acquisition module is used for acquiring an illumination layer corresponding to the image to be processed;
and the processing module is used for inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model and outputting an enhanced image.
Further, the building module is specifically configured to build a convolutional neural network model and a training set, and train the convolutional neural network model by using the training set to obtain the image-enhanced neural network model; the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image; the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image.
Further, the illumination layer corresponding to the image is obtained by the following method:
converting the image from a first color space to a second color space containing first luminance information, and extracting the first luminance information of the image;
filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image;
calculating an average value of second luminance information of all pixels of the gray-scale image;
and correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
Further, the filtering algorithm is a median filtering algorithm.
Further, the convolutional neural network model is a full convolutional network model.
A third aspect of the present application provides a display device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the image enhancement methods provided in the first aspect of the present application when executing the program.
A fourth aspect of the present application provides a computer storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the image enhancement methods provided by the first aspect of the present application.
According to the image enhancement method, the image enhancement device and the display equipment, the image enhancement neural network model is built, the input of the built image enhancement neural network model is the image and the illumination layer corresponding to the image, and the output of the built image enhancement neural network model is the enhanced image.
Drawings
FIG. 1 is a flowchart of a first embodiment of an image enhancement method of the present application;
FIG. 2 is a flow chart illustrating the construction of an image-augmented neural network model according to an exemplary embodiment of the present application;
FIG. 3 is a flowchart illustrating obtaining an illumination layer corresponding to an image according to an exemplary embodiment of the present application;
fig. 4 is a schematic diagram of an original image and an illumination layer corresponding to an acquired original image according to an exemplary embodiment of the present application;
fig. 5 is a schematic diagram of an enhanced image output after the original image in fig. 4 and the illumination image layer corresponding to the original image are input to the image enhanced neural network model;
fig. 6 is a hardware structure diagram of a display device where an image enhancement apparatus is located according to an exemplary embodiment of the present application;
fig. 7 is a schematic structural diagram of an image enhancement apparatus according to a first embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The application provides an image enhancement method, an image enhancement device and display equipment, and aims to solve the problem that the effect is poor when the existing method is used for enhancing a blackboard image.
According to the image enhancement method, the image enhancement device and the display equipment, the image enhancement neural network model is built, the input of the built image enhancement neural network model is the image and the illumination layer corresponding to the image, and the output of the built image enhancement neural network model is the enhanced image.
The technical aspects of the present application will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
Fig. 1 is a flowchart of a first embodiment of an image enhancement method according to the present application. The execution subject of the present embodiment may be a separate image enhancement device, or may be a display apparatus integrated with an image enhancement device. The following description will be given taking as an example a display apparatus in which an execution body is integrated with an image enhancement device. Referring to fig. 1, the method provided in this embodiment may include:
s101, constructing an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image.
Specifically, fig. 2 is a flowchart illustrating a process of constructing an image enhancement network model according to an exemplary embodiment of the present application. Referring to fig. 2, in this step, the image-enhanced neural network model may be constructed by the following method, which includes the following steps:
s201, a convolutional neural network model is constructed, the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image.
Specifically, the constructed convolutional neural network model may be a full convolutional network model. In addition, for a specific implementation process and an implementation principle for constructing the convolutional neural network model, reference may be made to descriptions in the prior art, and details are not described here.
The convolutional neural network model constructed in the present application is input as an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image. Therefore, the problem that the convolution kernel cannot capture the illumination change of the image in the existing model can be solved.
S202, constructing a training set, wherein the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image.
Specifically, 30 images may be selected, and then the illumination layer and the enhanced image corresponding to the 30 images are obtained. Specifically, a specific implementation method and an implementation principle for obtaining an illumination layer corresponding to an image will be described in detail in the following embodiments, and will not be described herein again. In this step, the enhanced images corresponding to the 30 images may be acquired by a conventional image enhancement method. Further, each image, the illumination layer corresponding to each image, and the enhanced image corresponding to each image may be divided into image blocks, for example, 9 image blocks, so that 270 sets of training data may be obtained.
And S203, training the convolutional neural network model by using the training set to obtain the image enhancement neural network model.
Specifically, the convolutional neural network model may be trained by using the training set by using an existing algorithm, so as to obtain the image-enhanced neural network model. For example, an error back-propagation algorithm may be employed to train the convolutional neural network model described above.
And S102, acquiring an illumination layer corresponding to the image to be processed.
Specifically, fig. 3 is a flowchart illustrating obtaining an illumination layer corresponding to an image according to an exemplary embodiment of the present application. Referring to fig. 3, the illumination layer of the image to be processed may be obtained by the following method, which may include the following steps:
s301, converting the image to be processed from the first color space to a second color space containing first brightness information, and extracting the first brightness information of the image to be processed.
Specifically, the first color space is a red, green, blue (RGB) color space or other color space. The second color space is a hue, saturation, brightness (HIS) color space, luminance, chrominance (YUV) or other color space containing luminance information. The following description will be given taking the first color space as the RGB color space and the second color space as the YUV color space as an example. In this step, the image to be processed is converted from the RGB color space to the YUV color space containing the first luminance information. Specifically, the image to be processed may be converted from an RGB color space to a YUV color space containing the first luminance information according to the following formula:
Y＝0.30R+0.59G+0.11B U＝0.493(B-Y)V＝0.877(R-Y)
further, after converting the image to be processed from the RGB color space to the YUV color space containing the first luminance information, the first luminance information of the image to be processed may be extracted.
And S302, filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image.
Specifically, in this step, a median filtering algorithm may be used to perform filtering processing on the first luminance information to obtain a grayscale image. For a specific principle of the median filtering algorithm, reference may be made to the description in the prior art, and details thereof are not repeated here.
S303, an average value of the second luminance information of all the pixels of the grayscale image is calculated.
Specifically, after the grayscale image is obtained, in this step, an average value of the second luminance information of all the pixels of the grayscale image is calculated. For example, the gray image includes 4 pixels, and the second luminance information of each pixel is
At this time, an average value of the second luminance information of all the pixels of the gray image may be calculated as a, where a ═ a + B + C + D)/4.
And S304, correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
Specifically, in this step, the second luminance information of each pixel in the grayscale image may be subtracted from the second luminance information of each pixel in the grayscale imageAnd averaging to obtain an illumination layer. With reference to the above example, the luminance information of each pixel of the illumination layer obtained after the correction is
Fig. 4 is a schematic diagram of an original image and an illumination layer corresponding to an acquired original image according to an exemplary embodiment of the present application. Referring to fig. 4, the image a in fig. 4 is an original image, and a grayscale image (the image B in fig. 4) is obtained after the original image is processed in steps S1021 and S1022, and further, an illumination layer (the image C in fig. 4) of the original image is obtained after steps S1023 and S1024.
S103, inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model, and outputting an enhanced image.
Specifically, fig. 5 is a schematic diagram of an enhanced image output after the original image in fig. 4 and the illumination layer corresponding to the original image are input to the image enhanced neural network model. As can be seen from FIG. 5, the enhancing effect is better after the method provided by the present application is adopted to enhance the blackboard image.
According to the image enhancement method and device, the image enhancement neural network model is built, the input of the built image enhancement neural network model is the image and the illumination layer corresponding to the image, and the output of the built image enhancement neural network model is the enhanced image.
Corresponding to the embodiment of the image enhancement method, the application also provides an embodiment of the image enhancement device.
The embodiment of the image enhancement device can be applied to display equipment. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. Taking a software implementation as an example, as a logical device, the device is formed by reading a corresponding computer program instruction in the non-volatile memory into the memory for operation through the processor of the display device where the device is located. From a hardware level, as shown in fig. 6, the hardware structure diagram of the display device where the image enhancement apparatus of the present application is located is shown, except for the memory 810, the processor 820 and the network interface 830 shown in fig. 6, the display device where the apparatus is located in the embodiment may also include other hardware according to the actual function of the image enhancement apparatus, which is not described again.
Referring to fig. 7, the image enhancement apparatus provided in the present application includes: a construction module 910, an acquisition module 920, and a processing module 930, wherein,
the constructing module 910 is configured to construct an image-enhanced neural network model, where an image and an illumination layer corresponding to the image are input to the image-enhanced neural network model, and an enhanced image is output;
the obtaining module 920 is configured to obtain an illumination layer corresponding to an image to be processed;
the processing module 930 is configured to input the image to be processed and the illumination layer corresponding to the image to be processed into the image-enhanced neural network model, and output an enhanced image.
Further, the constructing module 910 is specifically configured to construct a convolutional neural network model and a training set, and train the convolutional neural network model by using the training set to obtain the image-enhanced neural network model; the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image; the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image.
Further, the illumination layer corresponding to the image is obtained by the following method:
converting the image from a first color space to a second color space containing first luminance information, and extracting the first luminance information of the image;
filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image;
calculating an average value of second luminance information of all pixels of the gray-scale image;
and correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
Further, the filtering algorithm is a median filtering algorithm.
Further, the convolutional neural network model is a full convolutional network model.
With continued reference to fig. 6, the third aspect of the present application further provides a display device, which includes a memory 810, a processor 820 and a computer program stored in the memory and executable on the processor, wherein the processor 820 implements the steps of any image enhancement method provided in the present application when executing the program.
Specifically, the display device may include other hardware besides the memory 810, the processor 820 and the network interface 830 shown in fig. 6, which is not described again.
Furthermore, a display device suitable for executing a computer program comprises, for example, a general and/or special purpose microprocessor, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and/or a random access memory. The basic components of a display device include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, the display device will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, the display device does not necessarily have such a device. Further, the display device may be embedded in another device, such as a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
The fourth aspect of the present application also provides a computer storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of any of the image enhancement methods provided herein.
In particular, computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disk or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. A method of image enhancement, the method comprising:
constructing an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image;
acquiring an illumination layer corresponding to an image to be processed;
inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model, and outputting an enhanced image;
the illumination layer corresponding to the image is obtained by adopting the following method:
converting the image from a first color space to a second color space containing first luminance information, and extracting the first luminance information of the image;
filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image;
calculating an average value of second luminance information of all pixels of the gray-scale image;
and correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
2. The method according to claim 1, wherein the constructing the image-enhanced neural network model specifically comprises:
constructing a convolutional neural network model, wherein the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image;
constructing a training set, wherein the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image;
and training the convolutional neural network model by using the training set to obtain the image enhancement neural network model.
3. The method of claim 1, wherein the filtering algorithm is a median filtering algorithm.
4. The method of claim 2, wherein the convolutional neural network model is a full convolutional network model.
5. An image enhancement apparatus, comprising: a construction module, an acquisition module and a processing module, wherein,
the building module is used for building an image enhancement neural network model, wherein the input of the image enhancement neural network model is an image and an illumination layer corresponding to the image, and the output of the image enhancement neural network model is an enhanced image;
the acquisition module is used for acquiring an illumination layer corresponding to the image to be processed;
the processing module is used for inputting the image to be processed and the illumination layer corresponding to the image to be processed into the image enhancement neural network model and outputting an enhanced image;
the illumination layer corresponding to the image is obtained by adopting the following method:
converting the image from a first color space to a second color space containing first luminance information, and extracting the first luminance information of the image;
filtering the first brightness information by adopting a filtering algorithm to obtain a gray level image;
calculating an average value of second luminance information of all pixels of the gray-scale image;
and correcting the second brightness information of each pixel of the gray-scale image by adopting the average value to obtain an illumination layer, wherein the corrected brightness information of each pixel is equal to the second brightness information of each pixel minus the average value.
6. The apparatus according to claim 5, wherein the constructing module is specifically configured to construct a convolutional neural network model and a training set, and train the convolutional neural network model using the training set to obtain the image-enhanced neural network model; the input of the convolutional neural network model is an image and an illumination layer corresponding to the image, and the output of the convolutional neural network model is an enhanced image; the training set comprises a plurality of groups of training data, and each group of training data comprises an original image, a lighting image layer corresponding to the original image and an enhanced image corresponding to the original image.
7. A computer storage medium having a computer program stored thereon, the program, when being executed by a processor, performing the steps of the method of any one of claims 1 to 4.
8. A display device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-4 are implemented when the processor executes the program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810596553.6A CN109003231B (en) | 2018-06-11 | 2018-06-11 | Image enhancement method and device and display equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810596553.6A CN109003231B (en) | 2018-06-11 | 2018-06-11 | Image enhancement method and device and display equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109003231A CN109003231A (en) | 2018-12-14 |
| CN109003231B true CN109003231B (en) | 2021-01-29 |
Family
ID=64601211
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810596553.6A Active CN109003231B (en) | 2018-06-11 | 2018-06-11 | Image enhancement method and device and display equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109003231B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109741281B (en) * | 2019-01-04 | 2020-09-29 | Oppo广东移动通信有限公司 | Image processing method, image processing device, storage medium and terminal |
| CN109919869B (en) * | 2019-02-28 | 2021-06-04 | 腾讯科技（深圳）有限公司 | Image enhancement method and device and storage medium |
| CN110349107B (en) * | 2019-07-10 | 2023-05-26 | 北京字节跳动网络技术有限公司 | Image enhancement method, device, electronic equipment and storage medium |
| CN110428375B (en) * | 2019-07-24 | 2024-03-01 | 东软医疗***股份有限公司 | DR image processing method and device |
| CN113139909B (en) * | 2020-01-19 | 2022-08-02 | 杭州喔影网络科技有限公司 | Image enhancement method based on deep learning |
| CN111968188B (en) * | 2020-07-08 | 2023-08-22 | 华南理工大学 | Low-light image enhancement processing method, system, device and storage medium |
| CN112069977A (en) * | 2020-09-02 | 2020-12-11 | 腾讯科技（深圳）有限公司 | Image processing method, image processing device, electronic equipment and computer readable medium |
| CN115461777A (en) * | 2020-11-27 | 2022-12-09 | 京东方科技集团股份有限公司 | Computer-implemented image processing method, image-enhancing convolutional neural network, and computer product |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106846261A (en) * | 2016-12-21 | 2017-06-13 | 大连海事大学 | Underwater picture processing method based on convolutional neural networks |
| CN107578390A (en) * | 2017-09-14 | 2018-01-12 | 长沙全度影像科技有限公司 | A kind of method and device that image white balance correction is carried out using neutral net |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7187811B2 (en) * | 2003-03-18 | 2007-03-06 | Advanced & Wise Technology Corp. | Method for image resolution enhancement |
-
2018
- 2018-06-11 CN CN201810596553.6A patent/CN109003231B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106846261A (en) * | 2016-12-21 | 2017-06-13 | 大连海事大学 | Underwater picture processing method based on convolutional neural networks |
| CN107578390A (en) * | 2017-09-14 | 2018-01-12 | 长沙全度影像科技有限公司 | A kind of method and device that image white balance correction is carried out using neutral net |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109003231A (en) | 2018-12-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109003231B (en) | Image enhancement method and device and display equipment | |
| CN108875787B (en) | Image recognition method and device, computer equipment and storage medium | |
| CN104079818B (en) | Camera device, image processing system, camera system and image processing method | |
| JP2019028991A (en) | Target detection method, target detection apparatus, and image processing apparatus | |
| US20220207651A1 (en) | Method and apparatus for image processing | |
| US20220292635A1 (en) | Method and apparatus with image correction | |
| US8995784B2 (en) | Structure descriptors for image processing | |
| CN113052768B (en) | Method, terminal and computer readable storage medium for processing image | |
| CN114820581B (en) | Axisymmetric optical imaging parallel simulation method and device | |
| CN110083735B (en) | Image screening method and device, electronic equipment and computer readable storage medium | |
| US8509529B2 (en) | Color-image representative color decision apparatus and method of controlling operation thereof | |
| US20050271270A1 (en) | Method of determining color composition of an image | |
| CN103685854A (en) | Image processing apparatus, image processing method, and program | |
| US20230222639A1 (en) | Data processing method, system, and apparatus | |
| CN114202486B (en) | Mosaic removal method and system for capsule endoscope image | |
| CN111476731A (en) | Image correction method, image correction device, storage medium and electronic equipment | |
| Vazquez-Corral et al. | Simultaneous blind gamma estimation | |
| CN114897647B (en) | Teaching auxiliary system | |
| Buzzelli et al. | Consensus-driven illuminant estimation with GANs | |
| CN105991937A (en) | Virtual exposure method and device based on Bayer format image | |
| CN110322405B (en) | Video demosaicing method and related device based on self-encoder | |
| CN104954767B (en) | A kind of information processing method and electronic equipment | |
| KR20230013989A (en) | Image processing device, method of operation thereof, and image processing system comprising the same | |
| EP3062288A1 (en) | Method, apparatus and computer program product for reducing chromatic aberrations in deconvolved images | |
| CN111382772B (en) | Image processing method and device and terminal equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |