CN116994039A - Processing method and device for displaying image, storage medium and computer equipment - Google Patents
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Abstract
The invention discloses a processing method and a device for displaying images, a storage medium and computer equipment, which belong to the technical field of image processing and are suitable for the technical field of medical funding, and mainly aim to solve the problems of high time cost and high calculation cost in the image displaying stage in the prior art, and comprise the steps of acquiring images to be displayed and calculating the definition of the images to obtain the definition of the images to be displayed; identifying images to be classified which do not meet the first definition condition from the images to be displayed based on the definition of the images to be displayed; classifying the images to be classified to obtain target image categories; and identifying blurred images which do not meet the second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category; and performing deblurring treatment on the blurred image to obtain a clear image, and then performing image display.
Description
Technical Field
The invention relates to the technical field of image processing, and is suitable for the technical field of medical funding, in particular to a processing method and device for displaying images, a storage medium and computer equipment.
Background
In the field of medical funding, a funder needs to upload a plurality of photos for supporting the authenticity of the funding, such as natural images of patients, credentials, photos of medical materials, and the like. Because the medical funding field is the true display of individual cases, the definition of the image has certain requirements. However, due to the influence of factors such as the level of a photographer and the shooting environment, a certain amount of blurred pictures are contained in the uploaded pictures, and when the blurred pictures are transferred to a funding interface for display, certain adverse effects are caused.
At present, a unified image definition threshold is adopted to judge the definition of an image uploaded by a user, and the image display is carried out after the reconstruction processing is carried out on a blurred image which does not accord with the definition threshold. However, with the proliferation of the number of medical funding applicants, the number of blurred images obtained by adopting the existing display image processing method is too large, so that a large number of blurred images need to be reconstructed, and the time cost and the calculation cost of a medical funding system in the image display stage are increased.
Disclosure of Invention
In view of this, the present invention provides a method and apparatus for reconstructing a display image, a storage medium, and a computer device, and aims to solve the problems of high time cost and high calculation cost in the stage of displaying an image in the prior art.
According to one aspect of the present invention, there is provided a processing method of a presentation image, including:
acquiring an image to be displayed and calculating the definition of the image to be displayed to obtain the definition of the image to be displayed;
identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
and performing deblurring treatment on the blurred image to obtain a clear image, and then performing image display.
Further, the identifying, based on the image definition to be displayed, the image to be classified that does not satisfy the first definition condition from the images to be displayed includes:
determining a sharpness threshold maximum from a plurality of the image sharpness thresholds, and determining the first sharpness condition based on the sharpness threshold maximum;
if the definition of the image to be displayed is larger than the maximum value of the definition threshold, the first definition condition is met, and the image to be displayed is displayed;
if the definition of the image to be displayed is smaller than or equal to the maximum value of the definition threshold, the first definition condition is not met, and the image corresponding to the definition of the image to be displayed is determined to be the image to be classified.
Further, before the classifying processing is performed on the image to be classified to obtain the target image category, the method further includes:
acquiring a history display image dataset, wherein the history display image dataset comprises image category information;
and training the image classification model by adopting the historical display image data set to obtain a pre-training image classification model meeting classification precision.
Further, before the identifying, based on the target image category, a blurred image that does not meet a second sharpness condition from the images to be classified, the method further includes:
determining a corresponding target image sharpness threshold based on the target image category;
and determining the second sharpness condition based on the target image sharpness threshold.
Further, before the determining the corresponding target image sharpness threshold based on the target image category, the method further includes:
performing image definition calculation on each piece of image data in the historical display image data set to obtain historical display image definition corresponding to each piece of image data;
and classifying and counting the definition of the historical display image based on the image category information to respectively obtain image definition thresholds associated with the image category information.
Further, the identifying, based on the target image category, a blurred image that does not meet a second sharpness condition from the images to be classified includes:
if the definition of the image to be displayed is larger than the target image definition threshold, the second definition condition is met, and the image to be classified is displayed;
and if the definition of the image to be displayed is smaller than or equal to the target image definition threshold, the second definition condition is not satisfied, and the image corresponding to the definition of the image to be displayed is determined to be a blurred image.
Further, the obtaining the image to be displayed includes:
receiving service data to be displayed submitted by a target user terminal, and acquiring an image to be displayed in the service data to be displayed;
correspondingly, performing image display includes:
pushing the clear image to the target user terminal, and displaying the image after receiving a confirmation instruction fed back by the target user terminal.
According to another aspect of the present invention, there is provided a processing apparatus for displaying an image, comprising:
the definition calculating module is used for acquiring the image to be displayed and calculating the definition of the image to obtain the definition of the image to be displayed;
the first judging module is used for identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
the second judging module is used for classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
and the deblurring processing module is used for carrying out deblurring processing on the blurred image to obtain a clear image and then carrying out image display.
Further, the first judging module is further configured to:
determining a sharpness threshold maximum from a plurality of the image sharpness thresholds, and determining the first sharpness condition based on the sharpness threshold maximum;
if the definition of the image to be displayed is larger than the maximum value of the definition threshold, the first definition condition is met, and the image to be displayed is displayed;
if the definition of the image to be displayed is smaller than or equal to the maximum value of the definition threshold, the first definition condition is not met, and the image corresponding to the definition of the image to be displayed is determined to be the image to be classified.
Further, the device also comprises a model training module for:
acquiring a history display image dataset, wherein the history display image dataset comprises image category information;
and training the image classification model by adopting the historical display image data set to obtain a pre-training image classification model meeting classification precision.
Further, the second judging module is further configured to:
determining a corresponding target image sharpness threshold based on the target image category;
and determining the second sharpness condition based on the target image sharpness threshold.
Further, the second judging module is further configured to:
performing image definition calculation on each piece of image data in the historical display image data set to obtain historical display image definition corresponding to each piece of image data;
and classifying and counting the definition of the historical display image based on the image category information to respectively obtain image definition thresholds associated with the image category information.
Further, the second judging module is further configured to:
if the definition of the image to be displayed is larger than the target image definition threshold, the second definition condition is met, and the image to be classified is displayed;
and if the definition of the image to be displayed is smaller than or equal to the target image definition threshold, the second definition condition is not satisfied, and the image corresponding to the definition of the image to be displayed is determined to be a blurred image.
Further, the device further comprises an acquisition and display module for:
receiving service data to be displayed submitted by a target user terminal, and acquiring an image to be displayed in the service data to be displayed;
correspondingly, performing image display includes:
pushing the clear image to the target user terminal, and displaying the image after receiving a confirmation instruction fed back by the target user terminal.
According to still another aspect of the present invention, there is provided a storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the processing method for displaying an image as described above.
According to another aspect of the present invention, there is provided a computer device comprising a processor, a memory, a communication interface and a communication bus, said processor, said memory and said communication interface completing communication with each other via said communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the image display processing method.
By means of the technical scheme, the technical scheme provided by the embodiment of the invention has at least the following advantages:
compared with the prior art, the invention calculates the definition of the image to be displayed, compares the definition with first definition conditions determined by a plurality of definition thresholds, and determines the image which does not meet the first definition conditions as the image to be classified; classifying the images to be classified to obtain target image categories, determining second definition conditions based on target image definition thresholds corresponding to the target image categories, and identifying blurred images which do not meet the second definition conditions from the images to be classified; and finally, deblurring the blurred image to obtain a clear image and then displaying the image, so that multi-level judgment of the image to be displayed is realized, the blurred image is determined based on the multi-level judgment result, the problem that a great amount of work is brought to deblurring the image processing due to the fact that the image is subjected to fuzzy judgment by adopting a unified definition threshold value is avoided, and the calculation cost and the time cost of a medical funding service system can be effectively reduced.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 is a schematic flow chart of a processing method for displaying an image according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of another method for processing a display image according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of another method for processing a presentation image according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a processing device for displaying an image according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a processing method for displaying an image, as shown in fig. 1, the method comprises the following steps:
101. acquiring an image to be displayed and calculating the definition of the image to be displayed to obtain the definition of the image to be displayed;
in the embodiment of the invention, the current execution end acquires the image to be displayed and performs image definition calculation to obtain the image definition to be displayed. The image to be displayed is an image uploaded to the medical funding business system by a medical funding sponsor, such as a natural image of a patient, a certificate photograph, a medical material photograph and the like, and the embodiment of the invention is not particularly limited. The image definition calculating method includes a Brenner gradient method, a Tenegrad gradient method, a laplace gradient method, a variance method, an energy gradient method and the like, the embodiment of the invention is not particularly limited, and the calculating result is used for evaluating the image definition of the image to be displayed and can be represented by a specific numerical value.
102. Identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed;
in the embodiment of the invention, the current execution end identifies the image to be classified which does not meet the first definition condition from the images to be displayed based on the definition of the images to be displayed. The first definition condition is determined based on image definition thresholds corresponding to the image categories respectively. That is, the image sharpness thresholds have a one-to-one association with image categories, each having a corresponding one of the image sharpness thresholds. The image definition threshold is used for representing a numerical value for limiting the image definition, generally, when the image definition is larger than the image definition threshold, the image definition is considered to meet the requirement, and when the image definition is smaller than the image definition threshold, the image definition is considered to not meet the requirement, and the embodiment of the invention is not particularly limited. The first definition condition is used for representing a threshold limiting condition for judging the definition of the image to be displayed based on a plurality of image definition thresholds, and when the definition of the image to be displayed is larger than the threshold limiting condition, the corresponding image is directly displayed; and when the definition of the image to be displayed is smaller than the threshold limiting condition, the corresponding image is used as the image to be classified, and the image is further processed.
103. Classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
in the embodiment of the invention, the current execution end performs classification processing on the image to be classified to obtain the target image category. The specific image classification process can be performed manually or by using an image classification model. The image classification model includes VGG series model, res net series model, and the like, and the embodiment of the invention is not particularly limited. Because each image category corresponds to an image definition threshold, the target image definition threshold corresponding to the image to be classified is firstly determined, a second definition condition is determined based on the target image definition threshold, and whether the definition of the image to be displayed meets the second definition condition is judged to obtain a fuzzy image which does not meet the second definition condition. Unlike step 102, the second definition condition is only related to the target definition threshold, and when the definition of the image to be displayed is greater than the threshold limit condition determined by the target definition threshold, the corresponding image is directly displayed; when the definition of the image to be displayed is smaller than the threshold limiting condition determined by the target definition threshold, the corresponding image is used as a blurred image, and the image is further processed.
104. And performing deblurring treatment on the blurred image to obtain a clear image, and then performing image display.
In the embodiment of the invention, the current execution end performs deblurring treatment on the blurred image to obtain a clear image and then performs image display. The deblurring process can be performed by adopting a super resolution reconstruction (SRIR) method, and the super resolution reconstruction technology is an effective and low threshold method for improving the image resolution as the software method is adopted to improve the spatial resolution of the image without changing the original imaging equipment or improving the image shooting level of the camera. In the embodiment of the invention, a super-resolution reconstruction model based on a convolutional neural network, a super-resolution reconstruction model based on a wavelet domain local Gaussian model and the like can be adopted, and the embodiment of the invention is not particularly limited.
Further, as a refinement and expansion of the foregoing embodiment, in order to quickly determine that clear images from a large number of images to be displayed directly perform image display, and complete screening of first clear images, another processing method of displayed images is provided, as shown in fig. 2, where, based on the definition of the images to be displayed, the steps of identifying, from the images to be displayed, images to be classified that do not meet a first definition condition include:
201. determining a sharpness threshold maximum from a plurality of the image sharpness thresholds, and determining the first sharpness condition based on the sharpness threshold maximum;
in the embodiment of the invention, the current execution end determines the maximum value of the definition threshold from a plurality of image definition thresholds, for example, the maximum value of the definition threshold is determined from the image definition threshold r_1 associated with the material class image, the image definition threshold r_2 associated with the certificate class image and the image definition threshold r_3 associated with the natural image class image, and is recorded as max (r_1, r_2 and r_3). The current execution end determines a first sharpness condition based on a sharpness threshold maximum max (r_1, r_2, r_3).
202a, if the definition of the image to be displayed is larger than the maximum value of the definition threshold, the first definition condition is met, and the image to be displayed is displayed;
202b, if the definition of the image to be displayed is smaller than or equal to the maximum value of the definition threshold, the first definition condition is not met, and the image corresponding to the definition of the image to be displayed is determined to be the image to be classified.
In the embodiment of the invention, the current execution end sets that the definition of the image to be displayed is larger than the maximum value max (r_1, r_2 and r_3) of the definition threshold value to meet the first definition condition, and directly displays the image to be displayed; the current execution end sets the definition of the image to be displayed smaller than or equal to the definition threshold maximum value max (r_1, r_2 and r_3) to be incapable of meeting the first definition condition, determines the corresponding image as the image to be classified, and further processes the image in the later period.
Further, as a refinement and expansion of the specific implementation manner of the foregoing embodiment, in order to achieve automatic classification of an image to be displayed and improve the sharpness judging efficiency of the image to be displayed, as shown in fig. 3, another processing method of the image to be displayed is provided, and before the step of classifying the image to be classified, the method further includes:
301. acquiring a history display image dataset;
in the embodiment of the invention, a current execution end acquires a history display image data set, wherein the history display image data set contains image category information. The historical display image data set is an image to be displayed, such as a natural image, a certificate photograph, a medical material photograph and the like, of a patient, which is uploaded to a medical funding service system by a medical funding historical sponsor, and the embodiment of the invention is not particularly limited. The image category information is information for marking the categories of the images according to the image characteristics in advance, for example, the images are divided into three categories, including: the material class, the certificate class, and the natural image class may be identified by class 1, class 2, and class 3 for the image class of the material class image. In addition to using numbers to make category labels on images, letters, symbols, etc. may be used to make category labels, and embodiments of the present invention are not limited in detail.
302. And training the image classification model by adopting the historical display image data set to obtain a pre-training image classification model meeting classification precision.
In the embodiment of the invention, the image classification model is trained by adopting a historical display image data set. The image classification model includes VGG series model, res net series model, and the like, and the embodiment of the invention is not particularly limited. Specifically, when training a model, image information in a history display image dataset is used as input of the model, image category information is used as output of the model to perform model training, and finally a pre-training image classification model meeting classification accuracy is obtained.
Further, as a refinement and extension of the foregoing embodiment, in order to perform a distinction judgment on the sharpness of the images of different classes, another processing method for displaying the images is provided, where before the step of identifying, based on the target image class, a blurred image that does not meet the second sharpness condition from the images to be classified, the method further includes:
determining a corresponding target image sharpness threshold based on the target image category;
and determining the second sharpness condition based on the target image sharpness threshold.
In the embodiment of the invention, the current execution end adopts the pre-training image classification model to classify the images to be classified, if the data set of the historical image data set to be displayed trained by the pre-training image classification model contains three types of image data including material types, certificates and natural images, the pre-training image classification model can firstly obtain the similarity between the images to be classified and the three types of images including the material types, the certificates and the natural images when classifying the images to be classified, and then the category with the highest similarity is determined as the target image category when sorting the similarity. And determining a corresponding target image definition threshold based on the target image category, if the image to be displayed image_1 belongs to the certificate class, determining an image definition threshold r_2 associated with the certificate class image as the target image definition threshold, and determining a second definition condition based on the target image definition threshold r_2, if the target image definition threshold r_2 is directly used as a threshold definition condition, or multiplying the target image definition threshold r_2 by a certain coefficient to be used as a threshold definition condition, if a is used as a threshold definition condition, and the like, wherein the embodiment of the invention is not particularly limited.
Further, as a refinement and extension of the foregoing embodiment, in order to more generally learn to set an associated sharpness threshold for each image class, another method for processing a display image is provided, where before determining the corresponding target image sharpness threshold based on the target image class, the method further includes:
performing image definition calculation on each piece of image data in the historical display image data set to obtain historical display image definition corresponding to each piece of image data;
in the embodiment of the invention, the current execution end performs image definition calculation on each piece of image data in the historical display image data set to obtain the historical display image definition corresponding to each piece of image data. The image definition calculating method includes Brenner gradient method, tenegrad gradient method and the like, the embodiment of the invention is not particularly limited, and the calculation result is used for evaluating the image definition of the display image and can be represented by a specific numerical value.
And classifying and counting the definition of the historical display image based on the image category information to respectively obtain image definition thresholds associated with the image category information.
In the embodiment of the invention, the current execution terminal performs classification statistics on the definition of the historical display image based on the image type information, for example, based on the image type information of the material type image, acquires the definition of the historical display image of all the material type images, performs statistical analysis on the distribution condition of the definition of the historical display image of the material type image, and obtains an image definition threshold value associated with the material type image, which can be recorded as r_1; based on the image category information of the certificate images, acquiring the historic display image definition of all the certificate images, and then carrying out statistical analysis on the distribution condition of the historic display image definition of the certificate images to obtain an image definition threshold value associated with the certificate images, which can be recorded as r_2; based on the image category information of the natural image type images, the historic display image definition of all the natural image type images is obtained, and then the distribution condition of the historic display image definition of the natural image type images is subjected to statistical analysis to obtain an image definition threshold value associated with the natural image type images, which can be recorded as r_3 and the like.
Further, as a refinement and expansion of the specific implementation manner of the foregoing embodiment, in order to further perform second screening on an image to be classified, so as to facilitate direct display of an image meeting a corresponding definition condition, another method for processing a display image is provided, and the step of performing, based on the target image definition threshold, a second threshold judgment on the definition of the image to be displayed, where obtaining a blurred image that does not meet a second threshold judgment result includes:
if the definition of the image to be displayed is larger than the target image definition threshold, the second definition condition is met, and the image to be classified is displayed;
and if the definition of the image to be displayed is smaller than or equal to the target image definition threshold, the second definition condition is not satisfied, and the image corresponding to the definition of the image to be displayed is determined to be a blurred image.
In the embodiment of the invention, the current execution end compares the definition of the image to be displayed with a target image definition threshold value only, and if the image to be displayed image_1 belongs to the certificate class, the definition of the image to be displayed is compared with an image definition threshold value r_2 associated with the certificate class image only; if the image definition to be displayed is larger than the image definition threshold r_2, setting the image definition to meet a second definition condition, and directly displaying the image to be classified image_1; if the image definition to be displayed is smaller than or equal to the image definition threshold r_2, the second definition condition is set to be not satisfied, and the corresponding image image_1 to be displayed is determined to be a blurred image, etc., and the embodiment of the invention is not limited in detail.
Further, as a refinement and expansion of the specific implementation manner of the foregoing embodiment, in order to automatically obtain an image to be displayed and confirm and display a clear image after defuzzification processing, another method for processing a displayed image is provided, where the method further includes:
receiving service data to be displayed submitted by a target user terminal, and acquiring an image to be displayed in the service data to be displayed;
correspondingly, performing image display includes:
pushing the clear image to the target user terminal, and displaying the image after receiving a confirmation instruction fed back by the target user terminal.
In the embodiment of the invention, the current execution end receives the service data to be displayed submitted by the target user terminal, and acquires the image to be displayed from the service data to be displayed. The target user terminal is a terminal device, such as a mobile phone, a PC, etc., for a medical fund sponsor to initiate a medical fund application, and the embodiment of the present invention is not limited in detail. The service data to be displayed comprises data in the forms of images, characters, videos and the like, and the image data to be displayed in the form of images is obtained from the service data to be displayed.
Correspondingly, in the embodiment of the invention, when the current execution end displays the image, the clear image is pushed to the target user terminal corresponding to the medical funding sponsor, and whether the deblurred clear image is used or not is determined to the medical funding sponsor in the target user terminal. After confirming that the clear image is used on the target terminal, the target user terminal feeds back a confirmation instruction to the current execution end. And the current execution end receives the confirmation instruction fed back by the target user terminal and then displays the image.
Compared with the prior art, the method for processing the display image has the advantages that the definition of the image to be displayed is calculated, the definition is compared with first definition conditions determined by a plurality of definition thresholds, and the image which does not meet the first definition conditions is determined as the image to be classified; classifying the images to be classified to obtain target image categories, determining second definition conditions based on target image definition thresholds corresponding to the target image categories, and identifying blurred images which do not meet the second definition conditions from the images to be classified; and finally, deblurring the blurred image to obtain a clear image and then displaying the image, so that multi-level judgment of the image to be displayed is realized, the blurred image is determined based on the multi-level judgment result, the problem that a great amount of work is brought to deblurring the image processing due to the fact that the image is subjected to fuzzy judgment by adopting a unified definition threshold value is avoided, and the calculation cost and the time cost of a medical funding service system can be effectively reduced.
As an implementation of the method shown in fig. 1, an embodiment of the present invention provides a processing apparatus for displaying an image, as shown in fig. 4, where the apparatus includes:
the definition calculating module 41 is configured to obtain an image to be displayed and perform image definition calculation to obtain the image definition to be displayed;
a first judging module 42, configured to identify, from the images to be displayed, images to be classified that do not satisfy a first sharpness condition, based on the sharpness of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
a second judging module 43, configured to perform classification processing on the image to be classified to obtain a target image class; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
the deblurring processing module 44 is configured to deblur the blurred image, and display a clear image.
Further, the first judging module 42 is further configured to:
determining a sharpness threshold maximum from a plurality of the image sharpness thresholds, and determining the first sharpness condition based on the sharpness threshold maximum;
if the definition of the image to be displayed is larger than the maximum value of the definition threshold, the first definition condition is met, and the image to be displayed is displayed;
if the definition of the image to be displayed is smaller than or equal to the maximum value of the definition threshold, the first definition condition is not met, and the image corresponding to the definition of the image to be displayed is determined to be the image to be classified.
Further, the device also comprises a model training module for:
acquiring a history display image dataset, wherein the history display image dataset comprises image category information;
and training the image classification model by adopting the historical display image data set to obtain a pre-training image classification model meeting classification precision.
Further, the second judging module 43 is further configured to:
determining a corresponding target image sharpness threshold based on the target image category;
and determining the second sharpness condition based on the target image sharpness threshold.
Further, the second judging module 43 is further configured to:
performing image definition calculation on each piece of image data in the historical display image data set to obtain historical display image definition corresponding to each piece of image data;
and classifying and counting the definition of the historical display image based on the image category information to respectively obtain image definition thresholds associated with the image category information.
Further, the second judging module 43 is further configured to:
if the definition of the image to be displayed is larger than the target image definition threshold, the second definition condition is met, and the image to be classified is displayed;
and if the definition of the image to be displayed is smaller than or equal to the target image definition threshold, the second definition condition is not satisfied, and the image corresponding to the definition of the image to be displayed is determined to be a blurred image.
Further, the device further comprises an acquisition and display module for:
receiving service data to be displayed submitted by a target user terminal, and acquiring an image to be displayed in the service data to be displayed;
correspondingly, performing image display includes:
pushing the clear image to the target user terminal, and displaying the image after receiving a confirmation instruction fed back by the target user terminal.
Compared with the prior art, the method and the device for processing the display image have the advantages that the definition of the image to be displayed is calculated, the definition is compared with the first definition conditions determined by the definition thresholds, and the image which does not meet the first definition conditions is determined to be the image to be classified; classifying the images to be classified to obtain target image categories, determining second definition conditions based on target image definition thresholds corresponding to the target image categories, and identifying blurred images which do not meet the second definition conditions from the images to be classified; and finally, deblurring the blurred image to obtain a clear image and then displaying the image, so that multi-level judgment of the image to be displayed is realized, the blurred image is determined based on the multi-level judgment result, the problem that a great amount of work is brought to deblurring the image processing due to the fact that the image is subjected to fuzzy judgment by adopting a unified definition threshold value is avoided, and the calculation cost and the time cost of a medical funding service system can be effectively reduced.
According to one embodiment of the present invention, there is provided a storage medium storing at least one executable instruction for performing the method for processing a presentation image in any of the above-described method embodiments.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention, and the specific embodiment of the present invention is not limited to the specific implementation of the computer device.
As shown in fig. 5, the computer device may include: a processor 502, a communication interface (Communications Interface) 504, a memory 506, and a communication bus 508.
Wherein: processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.
A communication interface 504 for communicating with network elements of other devices, such as clients or other servers.
The processor 502 is configured to execute the program 510, and may specifically perform the relevant steps of the image display processing method described above.
In particular, program 510 may include program code including computer-operating instructions.
The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the computer device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
A memory 506 for storing a program 510. Memory 506 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may be specifically operable to cause the processor 502 to:
acquiring an image to be displayed and calculating the definition of the image to be displayed to obtain the definition of the image to be displayed;
identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
and performing deblurring treatment on the blurred image to obtain a clear image, and then performing image display.
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method of processing a presentation image, comprising:
acquiring an image to be displayed and calculating the definition of the image to be displayed to obtain the definition of the image to be displayed;
identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
and performing deblurring treatment on the blurred image to obtain a clear image, and then performing image display.
2. The method of claim 1, wherein the identifying, from the images to be displayed, images to be classified that do not satisfy a first sharpness condition based on the sharpness of the images to be displayed comprises:
determining a sharpness threshold maximum from a plurality of the image sharpness thresholds, and determining the first sharpness condition based on the sharpness threshold maximum;
if the definition of the image to be displayed is larger than the maximum value of the definition threshold, the first definition condition is met, and the image to be displayed is displayed;
if the definition of the image to be displayed is smaller than or equal to the maximum value of the definition threshold, the first definition condition is not met, and the image corresponding to the definition of the image to be displayed is determined to be the image to be classified.
3. The method according to claim 1, wherein before the classifying the image to be classified to obtain the target image class, the method further comprises:
acquiring a history display image dataset, wherein the history display image dataset comprises image category information;
and training the image classification model by adopting the historical display image data set to obtain a pre-training image classification model meeting classification precision.
4. The method of claim 1, wherein prior to identifying blurred images from the images to be classified that do not meet a second sharpness condition based on the target image category, the method further comprises:
determining a corresponding target image sharpness threshold based on the target image category;
and determining the second sharpness condition based on the target image sharpness threshold.
5. The method of claim 4, wherein prior to determining the corresponding target image sharpness threshold based on the target image category, the method further comprises:
performing image definition calculation on each piece of image data in the historical display image data set to obtain historical display image definition corresponding to each piece of image data;
and classifying and counting the definition of the historical display image based on the image category information to respectively obtain image definition thresholds associated with the image category information.
6. The method of claim 1, wherein the identifying, from the images to be classified, blurred images that do not meet a second sharpness condition based on the target image category comprises:
if the definition of the image to be displayed is larger than the target image definition threshold, the second definition condition is met, and the image to be classified is displayed;
and if the definition of the image to be displayed is smaller than or equal to the target image definition threshold, the second definition condition is not satisfied, and the image corresponding to the definition of the image to be displayed is determined to be a blurred image.
7. The method according to any one of claims 1 to 6, wherein the acquiring an image to be displayed comprises:
receiving service data to be displayed submitted by a target user terminal, and acquiring an image to be displayed in the service data to be displayed;
correspondingly, performing image display includes:
pushing the clear image to the target user terminal, and displaying the image after receiving a confirmation instruction fed back by the target user terminal.
8. A processing apparatus for displaying an image, comprising:
the definition calculating module is used for acquiring the image to be displayed and calculating the definition of the image to obtain the definition of the image to be displayed;
the first judging module is used for identifying images to be classified which do not meet a first definition condition from the images to be displayed based on the definition of the images to be displayed; the first definition condition is determined based on image definition thresholds corresponding to a plurality of image categories respectively;
the second judging module is used for classifying the images to be classified to obtain target image categories; identifying blurred images which do not meet a second definition condition from the images to be classified based on the target image category; the second definition condition is determined based on a target image definition threshold corresponding to the target image category;
and the deblurring processing module is used for carrying out deblurring processing on the blurred image to obtain a clear image and then carrying out image display.
9. A storage medium having stored therein at least one executable instruction for performing operations corresponding to the method of processing a presentation image as claimed in any one of claims 1 to 7.
10. A computer device comprising a processor, a memory, a communication interface and a communication bus, said processor, said memory and said communication interface completing communication with each other through said communication bus;
the memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform operations corresponding to the method for processing a presentation image according to any one of claims 1 to 7.
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