WO2020186888A1

WO2020186888A1 - Method and apparatus for constructing image processing model, and terminal device

Info

Publication number: WO2020186888A1
Application number: PCT/CN2019/130876
Authority: WO
Inventors: 乔宇; 何静雯; 董超
Original assignee: 深圳先进技术研究院
Priority date: 2019-03-21
Filing date: 2019-12-31
Publication date: 2020-09-24
Also published as: CN110047044A; CN110047044B

Abstract

A method and apparatus for constructing an image processing model, and a terminal device. The method comprises: configuring a start degradation level parameter of a base model based on a residual module, and training the configured base model to adjust a network parameter of the base model (S101); adding a feature adjusting layer into the base model, and generating an adaptive model (S102); configuring an end degradation level parameter of the adaptive model, and training the configured adaptive model to adjust parameters of the feature adjusting layer (S103); performing interpolation operation on the feature adjusting layer in the trained adaptive model, so that the finally formed image processing model can achieve image processing at any degradation level from the start degradation level to the end degradation level (S104). The method achieves continuous adjustability of restoration intensity, and the user experience is better.

Description

Method, device and terminal equipment for constructing image processing model

Technical field

The invention belongs to the technical field of image processing, and in particular relates to a method, device and terminal equipment for building an image processing model.

Background technique

Existing image restoration technologies based on deep learning all train a separate model for image restoration of a specific degradation level. If a model that does not match the degradation level is used to restore the degraded image, it will bring about an over-smoothing or over-sharpening effect, and the quality of the restored image will be poor, which will not meet the requirements of users.

In real life, the degradation levels of images are continuous. In this way, in order to solve the problem of restoration of degraded images, the traditional method can only train many image restoration models for different degradation levels, or train one large enough The image restoration model to solve a wide range of degraded images. However, this method will bring a very large amount of calculation and lack of flexibility.

technical problem

In view of this, the embodiments of the present invention provide an image processing model construction method, device, and terminal equipment to solve the problem that the existing image restoration processing has a single degradation level and lacks flexibility in processing degraded images with multiple degradation levels. .

Technical solutions

The first aspect of the embodiments of the present invention provides a method for constructing an image processing model, including:

Configure the initial degradation level parameters of the base model based on the residual module, and train the configured base model to adjust the network parameters of the base model. The base model includes a convolution layer, an activation function layer, and an image upsampling layer ；

Adding a feature adjustment layer to the trained base model to generate an adaptive model, where the feature adjustment layer is composed of multiple convolution kernels;

Configuring the end degradation level parameter of the adaptive model, and training the configured adaptive model to adjust the parameters of the feature adjustment layer;

Interpolation is performed on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the initial degradation level to the end degradation level.

A second aspect of the embodiments of the present invention provides an image processing model construction device, including:

The first configuration training unit is used to configure the initial degradation level parameters of the base model based on the residual module, and to train the configured base model to adjust the network parameters of the base model. The base model includes a convolutional layer, Activation function layer, image upsampling layer;

A model generation unit, configured to add a feature adjustment layer to the trained base model to generate an adaptive model, where the feature adjustment layer is composed of multiple convolution kernels;

The second configuration training unit is configured to configure the end degradation level parameters of the adaptive model, and train the configured adaptive model to adjust the parameters of the feature adjustment layer;

The model adjustment unit is used to perform interpolation operations on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the start degradation level to the end degradation level.

A third aspect of the embodiments of the present invention provides a terminal device, including:

A memory, a processor, and a computer program that is stored in the memory and can run on the processor, wherein the processor implements the image processing model provided in the first aspect of the embodiment of the present invention when the processor executes the computer program The steps of the construction method.

Wherein, the computer program includes:

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement the first aspect of the embodiments of the present invention Provide the steps of the method for constructing the image processing model.

Wherein, the computer program includes:

Beneficial effect

Compared with the prior art, the embodiment of the present invention has the beneficial effect of configuring the initial degradation level parameter of the base model based on the residual module, and training the configured base model to adjust the network parameters of the base model. , Then add the feature adjustment layer to the trained base model to generate an adaptive model, configure the end degradation level parameters of the adaptive model, and then train the configured adaptive model to adjust the feature adjustment Then, perform interpolation operation on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize the image processing of any degradation level from the start degradation level to the end degradation level, thereby Realizes the image restoration task of any degradation level, and realizes the continuous adjustment of the restoration intensity, and since no new image noise is introduced, the user can adjust the adjustment coefficient of the feature adjustment layer according to their preferences to achieve a satisfactory image processing effect , The user experience is better.

Description of the drawings

In order to explain the technical solutions in the embodiments of the present invention more clearly, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only of the present invention. For some embodiments, for those of ordinary skill in the art, other drawings can be obtained from these drawings without creative labor.

FIG. 1 is an implementation flowchart of an image processing model construction method provided by an embodiment of the present invention;

2 is a schematic diagram of a network architecture of a base model provided by an embodiment of the present invention;

3 is a schematic diagram of a network architecture of an adaptive model provided by an embodiment of the present invention;

4 is a schematic diagram of an image processing model construction device provided by an embodiment of the present invention;

Fig. 5 is a schematic diagram of a terminal device provided by an embodiment of the present invention.

Embodiments of the invention

In the following description, for the purpose of illustration rather than limitation, specific details such as a specific system structure and technology are proposed for a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, systems, circuits, and methods are omitted to avoid unnecessary details from obstructing the description of the present invention.

In order to illustrate the technical solution of the present invention, specific embodiments are used for description below. Please refer to FIG. 1. FIG. 1 shows an implementation process of an image processing model construction method provided by an embodiment of the present invention, which is detailed as follows:

In step S101, the initial degradation level parameter of the base model based on the residual module is configured, and the configured base model is trained to adjust the network parameters of the base model.

In the embodiment of the present invention, the base model based on the residual module is mainly used to solve the image restoration of the initial degradation level, which includes a convolution layer, an activation function layer, and an image upsampling layer.

Here, the choice of the network architecture of the base model is relatively flexible. Any convolutional network model that can handle image restoration tasks can be implemented. The embodiment of the present invention is preferably a network architecture similar to SRResNet with a residual module as the core. It is mainly composed of a convolutional layer, an activation function layer, and an image up-sampling layer. Please refer to Figure 2 for details. Among them, the stride of the first convolutional layer in the base model is 2, so that the image passing through the convolutional layer The length and width becomes 1/2 of the original, thereby improving the processing efficiency of the residual module, and after being processed by the residual module, it is input to the convolutional layer connected to the residual module, and the output image of the convolutional layer is input to The image is up-sampled to restore the original size. The model performs well in Gaussian denoising, super-resolution and JPEG lossy compression and restoration tasks.

After the base model with the residual module as the core is constructed, the pre-configured degradation level parameters are obtained, and the pre-configured degradation level parameters include a start degradation level parameter and an end degradation level parameter. Then according to the pre-configured degradation level parameters, configure the starting degradation level parameters of the base model.

Here, both the start degradation level parameter and the end degradation level parameter include: Gaussian noise parameters, JPEG compression quality parameters and bi-cubic downsampling parameters, where:

The initial degradation level parameters are: Gaussian noise, σ15; JPEG compression quality, q80; Bi-cubic downsampling, ×3;

The corresponding end degradation level parameters are: Gaussian noise, σ75; JPEG compression quality, q10; Bi-cubic downsampling, ×4.

It can be understood that the start degradation level parameter and the end degradation level parameter are not limited to the specific parameters mentioned above, and the specific parameters mentioned above are only an example and not specific specific parameters.

In step S102, the feature adjustment layer is added to the trained base model to generate an adaptive model.

In the embodiment of the present invention, the feature adjustment layer is composed of multiple convolution kernels. Since the depth-level convolution kernel is operated based on a single feature map, it is much faster than the general convolution kernel. Preferably, The feature adjustment layer is composed of multiple depthwise convolution filters.

It is understandable that, if the calculation speed of the base model is desired to be faster, the convolution kernel constituting the feature adjustment layer can be composed of a convolution kernel with a corresponding calculation speed, and is not limited to a depth-level convolution kernel.

Here, the size of the convolution kernel can be 1×1, 3×3, 5×5, 7×7, etc., which is not specifically limited here. Generally speaking, the larger the size of the convolution kernel, the better performance of the adaptive model on image restoration tasks that end the degradation level, but as the convolution kernel increases, this improvement is not obvious. Experiments show that in the task of JPEG lossy image restoration and Gaussian denoising, setting the size of the convolution kernel to 1×1 can get very ideal results. For image super-resolution tasks, the size of the convolution kernel must be set to at least 5×5.

Here, the adaptive model is mainly used to add a feature adjustment layer on the basis of the base model, so that the formed adaptive model can handle the image restoration task of the end degradation level, and because only the convolution kernel is added The feature adjustment layer is composed of not many network parameters. For example, for the feature adjustment layer with the convolution kernel size of 1×1 and 5×5, only 0.15% and 3.65% of the network parameters are added, which is not much. Increase the amount of calculation of the network model. Please refer to Figure 3 for the specific structure of the adaptive model. It is added after the convolutional layer of the base model or after the convolutional layer of the residual structure on the basis of the base model. The model formed by the feature adjustment layer, the residual structure referred to here is a structure composed of 16 residual modules, where the residual module includes a convolutional layer, and the activation layer connected to the convolutional layer, And another convolutional layer connected to the activation layer.

Optionally, step S102 is specifically:

The feature adjustment layer is placed after all the convolutional layers of the trained base model to generate an adaptive model.

Optionally, step S102 is specifically:

The feature adjustment layer is placed after the convolutional layer in the residual structure of the trained base model to generate an adaptive model.

In the embodiment of the present invention, in order to facilitate the adjustment of the feature map after the convolution operation, the feature adjustment layer needs to be placed behind the convolution layer. Specifically, it can be placed after all the convolution layers of the base model, or it can be placed on the base model. After the convolutional layer in the residual structure of the model, there is no specific limitation here.

Optionally, step S102 specifically includes:

Step S1021: Configure the size parameter of the convolution kernel of the feature adjustment layer.

Step S1022, adding the configured feature adjustment layer to the trained base model to generate an adaptive model.

In the embodiment of the present invention, due to different image processing tasks, the size of the convolution kernel in the corresponding feature adjustment layer is different. The size of the convolution kernel in the feature adjustment layer can be configured according to the actual situation, for example, by the adaptive model When the composed image processing model is to handle JPEG lossy image restoration and Gaussian denoising tasks, the size of the convolution kernel is set to 1×1; when the image super-resolution task is to be processed, the size of the convolution kernel is set to 5× 5 or more.

Here, the size parameters of the convolution kernel of the configuration feature adjustment layer can be pre-configured. For example, the image processing task for the user to be selected on the adaptive model interface can be found according to the comparison table of the image processing task and the configuration parameters Corresponding configuration parameters; it can also be configured by the user, and there is no specific limitation here.

In step S103, the end degradation level parameter of the adaptive model is configured, and the configured adaptive model is trained to adjust the parameters of the feature adjustment layer.

In the embodiment of the present invention, after the adaptive model is generated, the end degradation level parameter of the adaptive model is configured according to the acquired pre-configured degradation level parameter, and the end degradation level parameter of the adaptive model is configured. Training is performed so that the adaptive model can output a better quality restored image.

Here, in the process of training the adaptive model, only the parameters of the feature adjustment layer are adjusted, and the other network parameters remain unchanged, that is, during the training of the adaptive model, fix the adaptive model and The same network parameters of the base model are kept unchanged. The adaptive model is trained only by adjusting the parameters of the feature adjustment layer, and the training effect of image restoration is achieved after multiple training and adjustment of the parameters of the feature adjustment layer.

Here, in the process of training the base model and the adaptive model, the training samples used are the images corresponding to the corresponding degradation levels.

In step S104, interpolation is performed on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the start degradation level to the end degradation level.

In the embodiment of the present invention, when the adaptive model is trained and the parameters of the feature adjustment layer are adjusted to the optimal state, the adjustment test is performed on the adaptive model, specifically by testing all the feature adjustment layers in the adaptive model. Perform interpolation operation, so that the finally formed image processing model can realize the image restoration of any degradation level between "start" and "end", where "start" is the beginning of the degradation level, and "end" is the end of the degradation level. Interpolation is performed In essence, it can also be understood as multiplying the parameters of all the characteristic adjustment layers of the adaptive model by an adjustment coefficient. The adjustment coefficient ranges from 0 to 1. By changing the adjustment coefficient, you can continuously change the The restoration strength of an image with a certain degradation level. By changing the size of the adjustment coefficient, the restoration strength of the image with a given degradation level will also change accordingly, and if the degradation degree of the initial degradation level is less than that of the end degradation level, increase the adjustment coefficient, The higher the degree of restoration of degraded images.

Here, in the process of training the base model and the adaptive model, the convolutional neural network performs convolution processing on the degraded image in the training sample and the clear image corresponding to the degraded image to extract the unique features inside the image, and then The network parameters in the base model and the parameters of the feature adjustment layer in the adaptive model are trained and adjusted to learn the mapping relationship from the degraded image to the clear image. In the training process, the mean square error is used to calculate the error between the target image and the learned image, and then the network parameters are adjusted and updated through backpropagation. When the model converges, the network parameters reach the optimal after multiple optimization iterations Value.

In the embodiment of the present invention, the initial degradation level parameter of the base model based on the residual module is configured, and the configured base model is trained to adjust the network parameters of the base model, and then the feature adjustment layer is added to the economics In the trained base model, an adaptive model is generated, and the end degradation level parameters of the adaptive model are configured, and then the configured adaptive model is trained to adjust the parameters of the feature adjustment layer, and then the trained The feature adjustment layer in the adaptive model performs interpolation operations, so that the finally formed image processing model can realize image processing at any degradation level from the start degradation level to the end degradation level, thereby achieving the image restoration task at any degradation level , And realizes the continuous adjustment of restoration intensity, and because no new image noise is introduced, users can adjust the adjustment coefficient of the feature adjustment layer according to their preferences to achieve a satisfactory image processing effect, and the user experience is better.

It should be understood that the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution, and the execution sequence of each process should be controlled by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

Corresponding to the method for constructing an image processing model described in the above embodiment, FIG. 4 shows a schematic diagram of a device for constructing an image processing model provided by an embodiment of the present invention. The relevant part of the embodiment of the invention.

Referring to Figure 4, the device includes:

The first configuration training unit 41 is configured to configure the initial degradation level parameters of the base model based on the residual module, and train the configured base model to adjust the network parameters of the base model, the base model including a convolutional layer , Activation function layer, image upsampling layer;

The model generating unit 42 is configured to add a feature adjustment layer to the trained base model to generate an adaptive model, where the feature adjustment layer is composed of multiple convolution kernels;

The second configuration training unit 43 is configured to configure the end degradation level parameters of the adaptive model, and train the configured adaptive model to adjust the parameters of the feature adjustment layer;

The model adjustment unit 44 is configured to perform interpolation operations on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the start degradation level to the end degradation level .

Optionally, the model generating unit 42 is specifically configured to:

Optionally, both the start degradation level parameter and the end degradation level parameter include: Gaussian noise parameters, JPEG compression quality parameters, and bi-cubic downsampling parameters.

Optionally, the convolution kernel is a depth-level convolution kernel.

Optionally, the model generating unit 42 includes:

The convolution kernel configuration subunit is used to configure the size parameters of the convolution kernel of the feature adjustment layer;

The model generation word unit is used to add the configured feature adjustment layer to the trained base model to generate an adaptive model.

Fig. 5 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in FIG. 5, the terminal device 5 of this embodiment includes a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and running on the processor 50. When the processor 50 executes the computer program 52, the steps in the embodiment of the method for constructing each image processing model described above are implemented, for example, steps 101 to 104 shown in FIG. 1. Alternatively, when the processor 50 executes the computer program 52, the functions of the units in the foregoing system embodiments, such as the functions of the modules 41 to 44 shown in FIG. 4, are realized.

Exemplarily, the computer program 52 may be divided into one or more units, and the one or more units are stored in the memory 51 and executed by the processor 50 to complete the present invention. The one or more units may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 52 in the terminal device 5. For example, the computer program 52 can be divided into a first configuration training unit 41, a model generation unit 42, a second configuration training unit 43, and a model adjustment unit 44. The specific functions of each unit are as follows:

Optionally, the model generating unit 42 is specifically configured to:

Optionally, the convolution kernel is a depth-level convolution kernel.

Optionally, the model generating unit 42 includes:

The terminal device 5 may include, but is not limited to, a processor 50 and a memory 51. Those skilled in the art can understand that FIG. 5 is only an example of the terminal device 5, and does not constitute a limitation on the terminal device 5. It may include more or less components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

The so-called processor 50 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.

The memory 51 may be an internal storage unit of the terminal device 5, such as a hard disk or a memory of the terminal device 5. The memory 51 may also be an external storage device of the terminal device 5, for example, a plug-in hard disk equipped on the terminal device 5, a smart memory card (Smart Media Card, SMC), and a Secure Digital (SD) Card, Flash Card, etc. Further, the memory 51 may also include both an internal storage unit of the terminal device 5 and an external storage device. The memory 51 is used to store the computer program and other programs and data required by the terminal. The memory 51 can also be used to temporarily store data that has been output or will be output.

Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned functions can be allocated to different functional units and modules as required. Module completion, that is, divide the internal structure of the system into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist alone physically, or two or more units can be integrated into one unit. The above-mentioned integrated units can be hardware-based Formal realization can also be realized in the form of software functional units. In addition, the specific names of the functional units and modules are only used to facilitate distinguishing each other, and are not used to limit the protection scope of the present application. For the specific working process of the units and modules in the foregoing system, reference may be made to the corresponding process in the foregoing method embodiment, which is not repeated here.

In the foregoing embodiments, the description of each embodiment has its own focus. For parts that are not detailed or recorded in a certain embodiment, reference may be made to related descriptions of other embodiments.

A person of ordinary skill in the art may be aware that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed system/terminal device and method may be implemented in other ways. For example, the system/terminal device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units. Or components can be combined or integrated into another system, or some features can be omitted or not implemented. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, systems or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, the functional units in the various embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.

If the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, the steps of the foregoing method embodiments can be implemented. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may include: any entity or system capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunications signal, and software distribution media. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted in accordance with the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still implement the foregoing The technical solutions recorded in the examples are modified, or some of the technical features are equivalently replaced; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in Within the protection scope of the present invention.

Claims

A method for constructing an image processing model, characterized in that the method includes:

Configure the initial degradation level parameters of the base model based on the residual module, and train the configured base model to adjust the network parameters of the base model. The base model includes a convolution layer, an activation function layer, and an image upsampling layer ；

Adding a feature adjustment layer to the trained base model to generate an adaptive model, where the feature adjustment layer is composed of multiple convolution kernels;

Configuring the end degradation level parameter of the adaptive model, and training the configured adaptive model to adjust the parameters of the feature adjustment layer;

Interpolation is performed on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the initial degradation level to the end degradation level.
The method of claim 1, wherein the step of adding the feature adjustment layer to the trained base model to generate an adaptive model comprises:

The feature adjustment layer is placed after all the convolutional layers of the trained base model to generate an adaptive model.
The method of claim 1, wherein the step of adding the feature adjustment layer to the trained base model to generate an adaptive model comprises:

The feature adjustment layer is placed after the convolutional layer in the residual structure of the trained base model to generate an adaptive model.
The method according to claim 1, wherein the start degradation level parameter and the end degradation level parameter both include: Gaussian noise parameters, JPEG compression quality parameters, and bi-cubic downsampling parameters.
The method of claim 1, wherein the convolution kernel is a depth-level convolution kernel.
The method according to any one of claims 1 to 5, wherein the step of adding the feature adjustment layer to the trained base model to generate an adaptive model comprises:

Configuring the size parameter of the convolution kernel of the feature adjustment layer;

The configured feature adjustment layer is added to the trained base model to generate an adaptive model.
An image processing model building device, characterized in that the device includes:

The first configuration training unit is used to configure the initial degradation level parameters of the base model based on the residual module, and to train the configured base model to adjust the network parameters of the base model. The base model includes a convolutional layer, Activation function layer, image upsampling layer;

A model generation unit, configured to add a feature adjustment layer to the trained base model to generate an adaptive model, where the feature adjustment layer is composed of multiple convolution kernels;

The second configuration training unit is configured to configure the end degradation level parameters of the adaptive model, and train the configured adaptive model to adjust the parameters of the feature adjustment layer;

The model adjustment unit is used to perform interpolation operations on the feature adjustment layer in the trained adaptive model, so that the finally formed image processing model can realize image processing of any degradation level from the start degradation level to the end degradation level.
8. The image processing model building device according to claim 7, wherein the model generating unit is specifically configured to:

The feature adjustment layer is placed after all the convolutional layers of the trained base model to generate an adaptive model.
A terminal device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program as claimed in claims 1 to 6. Steps of any one of the image processing model construction methods.
A computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to realize the construction of the image processing model according to any one of claims 1 to 6 Method steps.