Ocular surface image optimization method and apparatus, portable ocular surface imager, and electronic device

Abstract

An ocular surface image optimization method and apparatus, a portable ocular surface imager, and an electronic device, relating to the technical field of multispectral imaging. The method comprises: acquiring a target number of real-time frames, and performing grid division on the real-time frames to obtain target grids on the real-time frames; calculating a preset modulation transfer function in the target grids to obtain sharpness values, and mapping the sharpness values of the target grids corresponding to a single real-time frame to form an image-quality spatial map of a target size; and using a differential fitting algorithm to synthesize the image-quality spatial maps corresponding to the real-time frames into one target image-quality spatial map, generating an optimized image on the basis of the target image-quality spatial map, and sending the optimized image to a portable ocular surface imager for output. By dividing the real-time frames into different grids and calculating the modulation transfer function on a per-grid basis, the calculation speed is increased, and the problem that optimized images cannot be calculated in real time on the basis of multiple frames of images is solved.

Description

A method, apparatus, portable ocular surface imager, and electronic device for optimizing ocular surface images.

[0001] This application claims priority to Chinese Patent Application No. 202510006093.7, filed on January 2, 2025, entitled "A Method, Apparatus, Portable Ocular Surface Imaging Device and Electronic Device for Optimizing Ocular Surface Images", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This invention relates to the field of multispectral imaging technology, and in particular to an ocular surface image optimization method, apparatus, portable ocular surface imager, and electronic device. Background Technology

[0003] In the field of multispectral imaging, especially in high-precision image acquisition of dynamic scenes, focusing remains a significant technical challenge. Current technologies typically assess image sharpness by calculating the MTF (Modulation Transfer Function) and then adjusting the focal length to maximize sharpness.

[0004] Currently, in scenarios with short focal lengths (focusing distance not exceeding 40mm), existing technologies calculate the MTF of each pixel and then synthesize multiple frames into an optimized image based on the calculation results. This method suffers from slow image processing speed and cannot calculate the optimized image in real time from multiple frames. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide an ocular surface image optimization method, apparatus, portable ocular surface imager, and electronic device, which can calculate the modulation transfer function on a grid-by-grid basis, thereby improving the calculation speed and solving the problem of not being able to calculate the optimized image in real time from multiple frames of images. The specific solution is as follows:

[0006] In a first aspect, this application provides an ocular surface image optimization method, applied to a mobile device, comprising:

[0007] The number of real-time frames of the target object obtained by the portable ocular surface imager are acquired, and the real-time frames are divided into grids to obtain each target grid on each real-time frame.

[0008] A preset modulation transfer function is calculated in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and the sharpness values ​​of each of the target grids corresponding to a single real-time frame are plotted as an imaging quality space of the target size.

[0009] The imaging quality spaces corresponding to each real-time frame are combined into a target imaging quality space using a differential fitting algorithm, and an optimized image is generated based on the target imaging quality space.

[0010] Optionally, calculating the preset modulation transfer function in each of the target grids includes:

[0011] Obtain the function calculation instruction corresponding to the preset modulation transfer function, and calculate the preset modulation transfer function in parallel in each of the target grids according to the function calculation instruction and general graphics processing technology.

[0012] Optionally, the step of combining the imaging quality spaces corresponding to each of the real-time frames into a single target imaging quality space using a difference fitting algorithm includes:

[0013] The difference fitting algorithm is used to analyze the sharpness values ​​corresponding to each of the target grids to obtain the sharpness change trend among the target grids;

[0014] The image quality spaces are synthesized based on the sharpness variation trend among the target grids to obtain the target image quality space.

[0015] Optionally, generating the optimized image based on the target imaging quality space includes:

[0016] The target imaging quality space is matched and synthesized with each of the imaging quality spaces in the local graphics processor cache to obtain the optimized image corresponding to the target object.

[0017] Optionally, before acquiring the number of real-time frames of the target object obtained by the portable ocular surface imager from image acquisition of the target object, the method further includes:

[0018] The spectral parameters of the portable ocular surface imager during image acquisition are obtained, and the target function parameter value corresponding to the initial modulation transfer function is determined based on the spectral parameters.

[0019] The initial modulation transfer function is adjusted according to the target function parameter values ​​to obtain the preset modulation transfer function.

[0020] Optionally, the image pixels of the optimized image are consistent with the image pixels of the real-time frame.

[0021] Secondly, this application provides an ocular surface image optimization method, applied to a portable ocular surface imager, comprising:

[0022] Obtain an image acquisition request, and acquire images of the target object according to the image acquisition request to obtain a target number of real-time frames corresponding to the target object;

[0023] Each of the real-time frames is sent to a mobile device, which then performs a grid division operation on each of the real-time frames. A preset modulation transfer function is calculated in each target grid obtained based on the grid division operation to obtain the sharpness values ​​corresponding to each target grid. The sharpness values ​​of each target grid corresponding to a single real-time frame are drawn into an image quality space of target size. Then, a difference fitting algorithm is used to combine the image quality spaces corresponding to each real-time frame into a target image quality space, and an optimized image is generated based on the target image quality space.

[0024] Thirdly, this application provides an ocular surface image optimization device, applied to a mobile device, comprising:

[0025] The grid division module is used to acquire the number of real-time frames of the target object obtained by the portable ocular surface imager in image acquisition of the target object, and to perform grid division operation on the real-time frames to obtain each target grid on each real-time frame.

[0026] The function calculation module is used to calculate a preset modulation transfer function in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and to draw the sharpness values ​​of each of the target grids corresponding to a single real-time frame as an image quality space of the target size.

[0027] The image transmission module is used to combine the imaging quality spaces corresponding to each real-time frame into a target imaging quality space using a differential fitting algorithm, and generate an optimized image based on the target imaging quality space.

[0028] Fourthly, this application provides a portable ocular surface imager, including an image acquisition device for acquiring images and a communication interface for communicating with a mobile device;

[0029] The portable ocular surface imager also includes a processor for executing computer programs to implement the aforementioned ocular surface image optimization method.

[0030] Fifthly, this application provides an electronic device, comprising:

[0031] Memory, used to store computer programs;

[0032] A processor for executing the computer program to implement the aforementioned ocular surface image optimization method.

[0033] In this application, the mobile device first acquires real-time frames of the target object obtained by a portable ocular surface imager, and performs a grid division operation on the real-time frames to obtain each target grid on each real-time frame. Then, a preset modulation transfer function is calculated in each target grid to obtain the sharpness value corresponding to each target grid. The sharpness values ​​of each target grid corresponding to a single real-time frame are plotted as an image quality space of the target size. Finally, a difference fitting algorithm is used to combine the image quality spaces corresponding to each real-time frame into a target image quality space, and an optimized image is generated based on the target image quality space. Therefore, this application obtains the sharpness value corresponding to each grid by performing a grid division operation on each real-time frame and calculating the modulation transfer function in each grid. By calculating the modulation transfer function on a grid-by-grid basis rather than on a pixel-by-pixel basis, the computational load is significantly reduced, thereby shortening the computation time required in the image optimization process and solving the problem of not being able to calculate the optimized image in real time from multiple frames. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0035] Figure 1 is a flowchart of an ocular surface image optimization method provided in this application;

[0036] Figure 2 is a flowchart of an ocular surface image optimization method provided in this application;

[0037] Figure 3 is a schematic diagram of the disassembled structure of a portable ocular surface imaging device provided in this application;

[0038] Figure 4 is a schematic diagram of the structure of a portable ocular surface imaging device component provided in this application;

[0039] Figure 5 is a schematic diagram of an ocular surface image optimization device provided in this application;

[0040] Figure 6 is a structural diagram of an electronic device provided in this application. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Currently, in short focal length scenarios, existing image optimization methods suffer from the inability to calculate the optimized image in real time from multiple frames. To address this, this application provides an ocular surface image optimization method that solves the problem of not being able to calculate the optimized image in real time from multiple frames by dividing the real-time frame into different grids and calculating the modulation transfer function on a grid-by-grid basis.

[0043] Referring to Figure 1, this embodiment of the invention discloses an ocular surface image optimization method, applied to a mobile device, comprising:

[0044] Step S11: Obtain the number of real-time frames of the target object obtained by the portable ocular surface imager in image acquisition of the target object, and perform a grid division operation on the real-time frames to obtain each target grid on each real-time frame.

[0045] In this embodiment, the mobile device first needs to be connected to the portable ocular surface imager to acquire real-time frames captured by the portable ocular surface imager. It should be noted that the real-time frames acquired by the mobile device can be multiple consecutive image frames acquired by the portable ocular surface imager, such as 3 consecutive images, or multiple non-consecutive image frames. Then, a grid division operation is performed on each real-time frame. In this embodiment, the grid size can be determined according to actual needs. For example, the real-time frame can be divided into a small grid of 40×30 pixels. Based on the pixel size of the real-time frame, each real-time frame can be divided into several grids, where each grid represents a local space. In this embodiment, the code for acquiring the real-time frame is frame_n = capture_frame(). The key code for performing the grid division operation on the real-time frame is shown below:

[0046] In this embodiment, before acquiring the real-time frames collected by the portable ocular surface imager, the method further includes: acquiring the spectral parameters of the portable ocular surface imager during image acquisition; determining the target function parameter value corresponding to the initial modulation transfer function based on the spectral parameters; and adjusting the initial modulation transfer function based on the target function parameter value to obtain a preset modulation transfer function. It is understood that the portable ocular surface imager uses different spectral parameters for image acquisition to adapt to the imaging needs of different scenarios, including infrared light, cobalt blue light, and white light. The modulation transfer function has corresponding function parameter values ​​under each spectral parameter; that is, different spectral parameters result in different function parameter values. By dividing each real-time frame into multiple grids, the workload of subsequent function calculation steps is reduced. By using the local segmentation grid MTF calculation method, at least three consecutive frames are synthesized into a clear image, and the algorithm processing time does not exceed 30ms, thus improving image processing efficiency.

[0047] Step S12: Calculate the preset modulation transfer function in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and draw the sharpness values ​​of each of the target grids corresponding to a single real-time frame as an imaging quality space of the target size.

[0048] In this embodiment, the process of calculating the preset modulation transfer function in each target grid may specifically include: obtaining the function calculation instruction corresponding to the preset modulation transfer function, and calculating the preset modulation transfer function in parallel in each target grid according to the function calculation instruction and general-purpose graphics processing unit (GPGPU) technology; that is, after receiving the calculation instruction, the MTF is calculated simultaneously in each target grid using GPGPU technology to obtain the sharpness value of each grid and record it as the initial imaging quality space; it should be noted that the working environment of the system in this embodiment is OpenGL or OpenGLES (a software interface), and the calculation task can be efficiently completed with the help of Compute Shader (a type of shader program in OpenGL) in the above-mentioned target working environment. In this embodiment, the key code for calculating the MTF in each grid is as follows:

[0049] The key code for plotting the imaging quality space is shown below:

[0050] In this embodiment, the sharpness values ​​of each target grid corresponding to a single real-time frame also need to be plotted as an imaging quality space of the target pixel size. For example, if there are 3 real-time frames, then 3 imaging quality spaces corresponding to the 3 real-time frames are obtained respectively. It should be noted that the pixel size of the obtained imaging quality space corresponds to the pixel size of the real-time frame, for example, 1600 pixels. The obtained total imaging quality space is used to intuitively reflect the sharpness distribution of the entire frame image. In this embodiment, the code for differential fitting to form the target quality space is as follows:

[0051] By calculating the imaging quality space for each real-time frame separately, the sharpness distribution of the image can be intuitively reflected. By calculating image sharpness in grid units, the method of calculating image sharpness in individual pixels is avoided, which greatly reduces the amount of system computation and improves work efficiency.

[0052] Step S13: Use the differential fitting algorithm to combine the imaging quality spaces corresponding to each real-time frame into a target imaging quality space, and generate an optimized image based on the target imaging quality space.

[0053] In this embodiment, the process of synthesizing the imaging quality space corresponding to each real-time frame image using a differential fitting algorithm specifically includes: analyzing the sharpness values ​​corresponding to each target grid using a differential fitting algorithm to obtain the sharpness variation trend between each target grid; synthesizing each imaging quality space based on the sharpness variation trend between each target grid to obtain the target imaging quality space; for example, if three consecutive real-time frames are obtained, after analyzing the sharpness variation trend between local grids using the differential fitting method, the imaging quality spaces of frames n, n+1, and n+2 are synthesized into a new quality space, i.e., the target imaging quality space, based on the analysis results. In this embodiment, if three real-time frames are obtained, the key code for performing the same grid division operation on the other two frames is shown below:

[0054] frames = [capture_frame() for _in range(2)]; / / Get frames n+1 and n+2.

[0055] quality_maps=[generate_quality_map([calculate_mtf(grid)for grid in divide_into_grids(frame)])for frame in frames];

[0056] In this embodiment, the process of generating an optimized image based on the target imaging quality space specifically includes: performing a matching and synthesis operation between the target imaging quality space and each imaging quality space in the local graphics processor cache to obtain the optimized image corresponding to the target object; that is, according to the new imaging quality space, i.e., the target imaging quality space, matching and synthesizing the original pixel information, i.e., each imaging quality space, in the GPU (Graphics Processing Unit) cache, and finally generating the optimized output image, i.e., the optimized image; it should be noted that the image pixels of the optimized output image are consistent with the image pixels of the real-time frame, both determined by the portable ocular surface imager. The code for synthesizing the optimized image is shown below:

[0057] The code that outputs the final optimized image is print("Output image generated.");

[0058] By matching the target imaging quality space with the imaging quality space corresponding to each real-time frame, the sharpness of the acquired optimized image is ensured to meet the requirements.

[0059] As can be seen, this application obtains the sharpness value corresponding to each grid by dividing each real-time frame into grids and calculating the modulation transfer function in each grid. By calculating the modulation transfer function in grids rather than pixels, the amount of computation is greatly reduced, thereby shortening the computation time required in the image optimization process and solving the problem of not being able to calculate the optimized image in real time from multiple frames.

[0060] Based on the previous embodiment, this application describes the specific process of image optimization handled by a mobile device during ocular surface image optimization. To make the technology of this invention more complete, the workflow of a portable ocular surface imager will be described next. Referring to Figure 2, this embodiment of the invention discloses an ocular surface image optimization method applied to a portable ocular surface imager, including:

[0061] Step S21: Obtain an image acquisition request, and acquire images of the target object according to the image acquisition request to obtain a target number of real-time frames corresponding to the target object.

[0062] In this embodiment, the real-time frame images acquired by the portable ocular surface imager using a multispectral camera can be acquired under infrared light, white light, or cobalt blue light. That is, the ocular surface imager in this embodiment includes an infrared lamp, an LED (Light Emitting Diode) lamp, and a cobalt blue lamp. The resolution of the real-time frames acquired by the imager is determined by the properties of the ocular surface imager itself, for example, an image resolution of 1600×1200. It is understood that the image acquisition device in the ocular surface imager of this embodiment carries a macro lens from the ocular surface camera for acquiring ocular surface images; the focal length of the macro lens is 20-40mm. It should be noted that the structure of the portable ocular surface imager in this embodiment also includes an infrared tracking module, which is used to track the positional relationship between the ocular surface and the macro lens, so that the ocular surface imager can acquire images more effectively.

[0063] Step S22: Send each of the real-time frames to the mobile device so that the mobile device can perform a grid division operation on each of the real-time frames, calculate a preset modulation transfer function in each target grid obtained based on the grid division operation to obtain each sharpness value corresponding to each target grid, and draw the sharpness values ​​of each target grid corresponding to a single real-time frame as an image quality space of target size, and then use a differential fitting algorithm to combine the image quality spaces corresponding to each of the real-time frames into a target image quality space, and generate an optimized image based on the target image quality space.

[0064] In this embodiment, the portable ocular surface imager needs to be connected to the mobile device so that the ocular surface imager can send the acquired real-time frames to the mobile device and perform image optimization operations on the mobile device. It is understood that the structure of the portable ocular surface imager also includes an auxiliary fixing sleeve, which can be used to connect and fix the ocular surface imager to the mobile device. It should be noted that the portable ocular surface imager may acquire dozens of real-time frame images, including real-time frames that do not meet the optimization conditions. Therefore, when sending real-time frames, multiple frames can be selected from the acquired real-time frames, such as 3 real-time frames that can improve image clarity, and the above 3 real-time frame images can be sent to the mobile device, instead of sending all the acquired real-time frames.

[0065] It should be noted that the structure of the portable ocular surface imager in this embodiment also includes the Placido mask, cushioning foam, portable ocular surface camera, and bracket with Bluetooth module as shown in Figure 3; the portable ocular surface imager after combining the Placido mask and lens is shown in Figure 4; through the Placido mask, in addition to image acquisition, the ocular surface imager can also project concentric ring images on the ocular surface and analyze the ocular surface condition based on the concentric ring images, thereby improving the application range of the portable ocular surface imager.

[0066] As can be seen, this application obtains the sharpness value corresponding to each grid by dividing each real-time frame into grids and calculating the modulation transfer function in each grid. By calculating the modulation transfer function in grids rather than pixels, the amount of computation is greatly reduced, thereby shortening the computation time required in the image optimization process and solving the problem of not being able to calculate the optimized image in real time from multiple frames.

[0067] Referring to Figure 5, an embodiment of the present invention discloses an ocular surface image optimization device, applied to a mobile device, comprising:

[0068] The grid division module 11 is used to acquire the number of real-time frames of the target object obtained by the portable ocular surface imager in image acquisition of the target object, and to perform grid division operation on the real-time frames to obtain each target grid on each real-time frame.

[0069] The function calculation module 12 is used to calculate a preset modulation transfer function in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and to draw the sharpness values ​​of each of the target grids corresponding to a single real-time frame as an imaging quality space of the target size.

[0070] The image sending module 13 is used to combine the imaging quality spaces corresponding to each real-time frame into a target imaging quality space using a differential fitting algorithm, and generate an optimized image based on the target imaging quality space.

[0071] As can be seen, this application obtains the sharpness value corresponding to each grid by dividing each real-time frame into grids and calculating the modulation transfer function in each grid. By calculating the modulation transfer function in grids rather than pixels, the amount of computation is greatly reduced, thereby shortening the computation time required in the image optimization process and solving the problem of not being able to calculate the optimized image in real time from multiple frames.

[0072] In some specific embodiments, the mesh division module 11 further includes:

[0073] The parameter value determination unit is used to acquire the spectral parameters when the portable ocular surface imager performs image acquisition, and determine the target function parameter value corresponding to the initial modulation transfer function based on the spectral parameters.

[0074] The function adjustment unit is used to adjust the initial modulation transfer function according to the target function parameter value to obtain the preset modulation transfer function.

[0075] In some specific embodiments, the function calculation module 12 specifically includes:

[0076] The function calculation unit is used to obtain the function calculation instructions corresponding to the preset modulation transfer function, and to calculate the preset modulation transfer function in parallel in each of the target grids according to the function calculation instructions and general graphics processing technology.

[0077] In some specific embodiments, the image sending module 13 specifically includes:

[0078] A sharpness analysis unit is used to analyze the sharpness values ​​corresponding to each of the target grids using the differential fitting algorithm, so as to obtain the sharpness change trend among the target grids.

[0079] A quality space acquisition unit is used to synthesize each of the imaging quality spaces based on the sharpness variation trend among the target grids to obtain the target imaging quality space.

[0080] In some specific embodiments, the image sending module 13 specifically includes:

[0081] An image acquisition unit is used to perform a matching and synthesis operation between the target imaging quality space and each of the imaging quality spaces in the local graphics processor cache, so as to obtain the optimized image corresponding to the target object.

[0082] Furthermore, this application also discloses an electronic device. FIG6 is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content in the figure should not be considered as any limitation on the scope of use of this application.

[0083] Figure 6 is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the ocular surface image optimization method disclosed in any of the foregoing embodiments. Furthermore, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0084] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0085] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0086] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the ocular surface image optimization method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include a computer program capable of performing other specific tasks.

[0087] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned ocular surface image optimization method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0089] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0090] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0091] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing ocular surface images, characterized in that, Applied to mobile devices, including: The number of real-time frames of the target object obtained by the portable ocular surface imager are acquired, and the real-time frames are divided into grids to obtain each target grid on each real-time frame. A preset modulation transfer function is calculated in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and the sharpness values ​​of each of the target grids corresponding to a single real-time frame are plotted as an imaging quality space of the target size. The imaging quality spaces corresponding to each real-time frame are combined into a target imaging quality space using a differential fitting algorithm, and an optimized image is generated based on the target imaging quality space.

2. The ocular surface image optimization method according to claim 1, characterized in that, The calculation of the preset modulation transfer function in each of the target grids includes: Obtain the function calculation instruction corresponding to the preset modulation transfer function, and calculate the preset modulation transfer function in parallel in each of the target grids according to the function calculation instruction and general graphics processing technology.

3. The ocular surface image optimization method according to claim 1, characterized in that, The step of combining the imaging quality spaces corresponding to each real-time frame into a single target imaging quality space using a differential fitting algorithm includes: The difference fitting algorithm is used to analyze the sharpness values ​​corresponding to each of the target grids to obtain the sharpness change trend among the target grids; The imaging quality spaces are synthesized based on the sharpness variation trend among the target grids to obtain the target imaging quality space.

4. The ocular surface image optimization method according to claim 1, characterized in that, The process of generating an optimized image based on the target imaging quality space includes: The target imaging quality space is matched and synthesized with each of the imaging quality spaces in the local graphics processor cache to obtain the optimized image corresponding to the target object.

5. The ocular surface image optimization method according to any one of claims 1 to 4, characterized in that, Before acquiring the number of real-time frames of the target object obtained by the portable ocular surface imager from image acquisition of the target object, the method further includes: The spectral parameters of the portable ocular surface imager during image acquisition are obtained, and the target function parameter value corresponding to the initial modulation transfer function is determined based on the spectral parameters. The initial modulation transfer function is adjusted according to the target function parameter values ​​to obtain the preset modulation transfer function.

6. The ocular surface image optimization method according to claim 1, characterized in that, The optimized image has the same pixel resolution as the real-time frame.

7. A method for optimizing ocular surface images, characterized in that, Applications in portable ocular surface imaging devices include: Obtain an image acquisition request, and acquire images of the target object according to the image acquisition request to obtain a target number of real-time frames corresponding to the target object; Each of the real-time frames is sent to a mobile device, which then performs a grid division operation on each of the real-time frames. A preset modulation transfer function is calculated in each target grid obtained based on the grid division operation to obtain the sharpness values ​​corresponding to each target grid. The sharpness values ​​of each target grid corresponding to a single real-time frame are drawn into an image quality space of target size. Then, a difference fitting algorithm is used to combine the image quality spaces corresponding to each real-time frame into a target image quality space, and an optimized image is generated based on the target image quality space.

8. An ocular surface image optimization device, characterized in that, Applied to mobile devices, including: The grid division module is used to acquire the number of real-time frames of the target object obtained by the portable ocular surface imager in image acquisition of the target object, and to perform grid division operation on the real-time frames to obtain each target grid on each real-time frame. The function calculation module is used to calculate a preset modulation transfer function in each of the target grids to obtain the sharpness values ​​corresponding to each of the target grids, and to draw the sharpness values ​​of each of the target grids corresponding to a single real-time frame as an image quality space of the target size. The image transmission module is used to combine the imaging quality spaces corresponding to each real-time frame into a target imaging quality space using a differential fitting algorithm, and generate an optimized image based on the target imaging quality space.

9. A portable ocular surface imaging device, characterized in that, It includes an image acquisition device for acquiring images and a communication interface for communicating with mobile devices; The portable ocular surface imager further includes a processor for executing a computer program to implement the ocular surface image optimization method as described in claim 7.

10. An electronic device, characterized in that, For use with a memory and a processor; wherein the memory is used to store a computer program, and the processor is used to execute the computer program to implement the ocular surface image optimization method as described in any one of claims 1 to 7.

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