An Image Enhancement Method, System and Device during the Shooting Process of an Action Camera

Abstract

The present invention discloses an image enhancement method, system and device during the shooting process of a sports camera, which relates to the technical field of image processing. The method includes: collecting the current environmental light data and motion state data collected by the sports camera; performing adaptive frame rate weight calculation on the environmental light data and motion state data to generate a set of key frame weights; performing a histogram equalization algorithm on the set of key frame weights to obtain an equalized key frame set; using motion compensation technology to perform key frame motion compensation fusion processing on the equalized key frame set to form a fused image; performing image edge enhancement on the fused image and displaying the image after image edge enhancement to the user for real-time preview. Through the adaptive frame rate weight calculation and motion compensation fusion processing technology, the present invention effectively alleviates the loss of image quality caused by sudden changes in light, reduces the blurriness of imaging, enables stable and clear images to be output even during high-speed movement, and ensures the stability and color accuracy of the images.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an image enhancement method, system and device during the shooting process of an action camera. Background Art

[0002] In today's action camera applications, users often face two major technical challenges: one is the significant degradation of image quality under rapidly changing lighting conditions, especially in outdoor sports such as skiing and mountain biking, where the instantaneous transition from shadow to direct sunlight results in uneven exposure and color distortion; the other is the instability and blurriness of images during intense motion, as the vibrations and rapid movement caused by the motion make it difficult to keep the picture clear. Traditional image processing methods often focus on solving single problems, such as simply adjusting the frame rate or using simple image stabilization techniques, lacking effective coping strategies for complex lighting changes and high-speed motion scenarios. Therefore, there is an urgent need in the market for an image enhancement technology that can adaptively adjust and simultaneously optimize lighting processing and motion compensation to improve the shooting performance of action cameras under various extreme conditions. Summary of the Invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides an image enhancement method during the shooting process of an action camera to solve

[0005] the problem of image quality degradation caused by rapidly changing lighting conditions and intense motion environments.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, an embodiment of the present invention provides an image enhancement method during the shooting process of an action camera, which includes collecting current environmental lighting data and motion state data of the action camera; calculating an adaptive frame rate weight for the environmental lighting data and motion state data to generate a set of key frame weights; performing a histogram equalization algorithm on the set of key frame weights to obtain an equalized key frame set; using motion compensation technology to perform key frame motion compensation fusion processing on the equalized key frame set to form a fused image; performing image edge enhancement on the fused image and displaying the image after image edge enhancement to the user for preview in real time.

[0008] As a preferred solution of the image enhancement method during the shooting process of the action camera of the present invention, the environmental lighting data and motion state data include: light intensity, color information, directional information, light distribution, position data, rotation data, acceleration, timestamp, motion trajectory, zoom information.

[0009] As a preferred solution of the image enhancement method during the shooting process of the sports camera of the present invention, wherein: perform adaptive frame rate weight calculation on the environmental light data and the motion state data to generate a key frame weight set, and the specific steps are as follows:

[0010] Perform adaptive frame rate weight calculation on each frame k in the environmental light data and the motion state data sequence, and the adaptive frame rate weight calculation expression is:

[0011]

[0012] Wherein, is the weight value of the th frame, is the size of the window, is the inverse function of the hyperbolic sine function, is the absolute value of the light difference between the jth frame and the reference frame, is the absolute value of the light difference between the kth frame and the reference frame, is a scalar coefficient, is a control factor, is the motion intensity index of the jth frame;

[0013] Collect the weight values calculated for each frame to form a key frame weight set V, and the key frame weight set V expression is: .

[0014] As a preferred solution of the image enhancement method during the shooting process of the sports camera of the present invention, wherein: perform histogram equalization algorithm on the key frame weight set to obtain an equalized key frame set, and the specific steps are as follows:

[0015] Perform normalization processing on all elements in the weight set , and the normalization processing expression is:

[0016]

[0017] Wherein, is the weight value of the th frame after normalization;

[0018] Use the normalized weight values to construct a cumulative distribution function, and the cumulative distribution function expression is:

[0019]

[0020] Wherein, is the result of accumulating the first k normalized weight values, k is an index variable, and i is a loop variable;

[0021] Calculate the weight value of each th frame after normalization The corresponding cumulative probability , where ;

[0022] Based on the cumulative probability , the equalized weight is obtained by linear interpolation ;

[0023] For the equalized weight value Inverse normalize back to The scale range of the weight value, and summarize to form the set of equalized key-frame weights:

[0024]

[0025] Among them, is the set of equalized key-frame weights.

[0026] As a preferred solution of the image enhancement method during the shooting process of the action camera described in the present invention, wherein: the key-frame motion compensation fusion process is performed on the set of equalized key frames by using the motion compensation technology to form a fused image, and the specific steps are as follows:

[0027] For each pair of adjacent key frames in the set of equalized key-frame weights , The optical flow field between them is estimated by using the long-distance image block matching algorithm, and the optical flow field expression is:

[0028]

[0029] Among them, is the optical flow field function from the k-th frame to the k+1-th frame, is the position of the pixel in the image, is the moving distance of the pixel along the x-axis, is the moving distance of the pixel along the y-axis;

[0030] Based on the optical flow field, motion compensation is performed on the non-key frames, and the motion compensation expression is:

[0031]

[0032] Among them, is the image of the m-th frame after motion compensation, is the image of the m-th frame before being processed;

[0033] The images after motion compensation are fused, and the fusion function expression is:

[0034]

[0035] Among them, is the fusion function, is the k-th key image after equalization processing, and is the control factor, is the sharpness index, is the attenuation factor;

[0036] Cumulatively generate the final fused image for the fusion function, and the cumulative expression is:

[0037]

[0038] wherein, is the finally obtained fused image, is the motion compensation frame corresponding to the key frame k.

[0039] As a preferred solution of the image enhancement method during the shooting process of the motion camera described in the present invention, wherein: the fused image is subjected to image edge enhancement, and the image after image edge enhancement is displayed to the user for preview in real time. The specific steps are as follows:

[0040] Preprocess the fused image using bilateral filtering. The bilateral filtering expression is:

[0041]

[0042] wherein, is the new pixel value of the pixel point after bilateral filtering processing, is the original pixel value of the fused image at the coordinate , is the spatial weight function, is the range weight function, is the distance between the abscissa of the pixel position s in the neighborhood and the center point x. s and t respectively represent the position coordinates of the pixels in this neighborhood, is a neighborhood;

[0043] Apply an edge detection algorithm to the image after bilateral filtering processing to enhance the edges.

[0044] As a preferred solution of the image enhancement method during the shooting process of the motion camera described in the present invention, wherein: the edge detection algorithm, the specific steps are as follows:

[0045] Further smooth the image to reduce noise interference;

[0046] Calculate the gradient magnitude G and direction of each pixel of the image ;

[0047] Only retain the edges in the direction of the local maximum gradient;

[0048] According to the image dynamically adjust the threshold based on the average brightness and contrast of the image to complete the edge-enhanced image;

[0049]

[0050] wherein, is the high threshold, is the low threshold, is the average brightness of the image, is the adjustment factor, is the image contrast, is the difference factor, k and are constants used to distinguish strong edges and weak edges;

[0051] Finally, the edge-enhanced image is rendered in real time and displayed to the user for preview through the display screen of the motion camera.

[0052] In a second aspect, the present invention provides an image enhancement system during the shooting process of a motion camera, including an environmental perception and motion monitoring module, an adaptive weight calculation module, an image processing optimization module, and a real-time preview module; the environmental perception and motion monitoring module is used to collect current environmental light data and motion state data through an integrated environmental light sensor and a motion detection module in the motion camera; the adaptive weight calculation module is used to perform adaptive frame rate weight calculation on the environmental light data and motion state data to generate a key frame weight set; the image processing optimization module is used to perform a histogram equalization algorithm on the key frame weight set to obtain an equalized key frame set, and use motion compensation technology to perform key frame motion compensation fusion processing on the equalized key frame set to form a fused image; the real-time preview module is used to display the edge-enhanced image to the user for preview in real time.

[0053] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, it implements any step of the image enhancement method during the shooting process of a motion camera as described in the first aspect of the present invention.

[0054] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, it implements any step of the image enhancement method during the shooting process of a motion camera as described in the first aspect of the present invention.

[0055] The beneficial effects of the present invention are as follows: By integrating environmental perception and dynamic data analysis, the main problems of uneven image exposure, color distortion, and blurred images in the current technology are effectively solved; Through adaptive frame rate weight calculation, the key frame selection is intelligently adjusted according to the real-time light intensity and motion state, effectively alleviating the image quality loss caused by sudden light changes, ensuring the continuity and color accuracy of the video. Secondly, combined with the motion compensation fusion processing technology, the motion blur is significantly reduced, enabling a stable and clear picture to be output even during high-speed movement; In addition, the visual effect of the image is further enhanced through histogram equalization and edge enhancement steps, enhancing the detail expressiveness, making the finally presented video content more vivid and professional. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0057] Figure 1 It is a flowchart of the image enhancement method during the shooting process of the action camera in Embodiment 1.

[0058] Figure 2 It is a flowchart of forming a fused image in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the following will make a detailed description of the specific embodiments of the present invention in conjunction with the drawings of the specification.

[0060] Many specific details are set forth in the following description in order to fully understand the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0061] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0062] Embodiment 1

[0063] Refer to Figure 1 and Figure 2, which is the first embodiment of the present invention. This embodiment provides an image enhancement method during the shooting process of a sports camera, including the following steps:

[0064] S1. Collect the current environmental light data and motion state data collected by the sports camera.

[0065] Furthermore, the environmental light data and motion state data include: light intensity, color information, directional information, light distribution, position data, rotation data, acceleration, timestamp, motion trajectory, zoom information.

[0066] Furthermore, perform adaptive frame rate weight calculation on each frame k in the environmental light data and motion state data sequence to reflect the importance of the current frame under light changes and motion states. The expression for adaptive frame rate weight calculation is:

[0067]

[0068] Where, is the weight value of the th frame, is the size of the window, is the inverse function of the hyperbolic sine function, which helps to adapt to the changes in light differences, is the absolute value of the light difference between the jth frame and the reference frame, is the absolute value of the light difference between the kth frame and the reference frame, is a scalar coefficient used to control the sensitivity of the influence of light differences on weight calculation, The larger the value of , the smaller the influence of light differences on the weight, and vice versa. It helps to adjust the proportion of light changes in the overall evaluation to ensure appropriate attention under different light conditions, is a control factor, and this parameter controls the influence degree of the motion intensity index on the weight The larger the

[0069] Collect the weight values calculated for each frame to form a key frame weight set V. The expression for the key frame weight set V is: .

[0070] It should be noted that a "reference frame" refers to a selected reference frame in a sequence, which is used to compare the lighting differences with other frames. This frame is usually selected as the one with relatively stable lighting conditions or representing the typical lighting conditions in the sequence. Selecting the reference frame as the basis for comparison can quantify the deviation of each frame image from the ideal or standard lighting conditions, thereby helping the system understand the degree of change in lighting conditions for each frame. That is, it represents the absolute value of the difference between the lighting intensity of the j-th frame image being currently processed and the reference frame. This difference can reflect the degree of light change, which is crucial for video processing, image analysis, and tasks that require lighting consistency processing such as video stabilization and color correction.

[0071] It should also be noted that by comprehensively considering the ambient light and motion state information, a weight value is assigned to each frame in the video sequence to reflect its importance for subsequent processing such as video compression, analysis, and stabilization. The calculation formula utilizes the lighting difference and the motion intensity as well as the adjustment parameters and to ensure that the weights can adapt to changes in different scenarios. Through such calculations, frames that are more stable in terms of lighting and dynamics can be identified as key frames. Collecting these weights forms the key frame weight set V, providing a basis for subsequent processing.

[0072] S2. Perform adaptive frame rate weight calculation on the ambient light data and motion state data to generate the key frame weight set.

[0073] Furthermore, perform the histogram equalization algorithm on the key frame weight set to obtain the equalized key frame set. Using the histogram equalization algorithm, by redistributing the gray values of the pixels, the details in the darker and brighter regions of the image can be improved, thereby enhancing the contrast of the entire image. The specific steps are as follows:

[0074] Perform normalization processing on all elements in the weight set . The normalization processing expression is:

[0075]

[0076] where is the weight value of the -th frame after normalization, , respectively represent the minimum and maximum values in the weight set V, which are used to determine the normalization range to ensure that all weight values are mapped to the interval;

[0077] Construct a cumulative distribution function using the normalized weight values, so that the weights of different datasets or different scales can be directly compared, facilitating the debugging and optimization of the algorithm. The expression of the cumulative distribution function is:

[0078]

[0079] where is the result of accumulating the first k normalized weight values, k is the index variable, and i is the loop variable;

[0080] Furthermore, calculate the cumulative probability corresponding to the weight value of the -th frame after normalization, where ;

[0081] Based on the cumulative probability obtain the equalized weight through linear interpolation;

[0082] Inverse normalize the equalized weight value back to the scale range of the weight value, and summarize to form the equalized key-frame weight set:

[0083]

[0084] where is the equalized key-frame weight set.

[0085] It should be noted that histogram equalization is a technique in image processing used to improve the global contrast of an image, making it look more uniform. When applied to the key-frame weight set, its purpose is to make the weight distribution more uniform, avoiding processing imbalances caused by some weights being too high or too low. Through normalization, constructing a cumulative distribution function, calculating the cumulative probability, and linear interpolation, the equalization of the weight distribution is finally achieved, enabling a more reasonable weight transition between important frames and less important frames, thereby optimizing the key-frame selection process.

[0086] S3. Use motion compensation technology to perform key-frame motion compensation fusion processing on the equalized key-frame set to form a fused image.

[0087] Furthermore, for each pair of adjacent key frames , in the equalized key-frame weight set, use the long-distance image block matching algorithm to estimate the optical flow field between them. The expression of the optical flow field is:

[0088]

[0089] where is the optical flow field function from the k-th frame to the (k + 1)-th frame, is the position of the pixel in the image, is the moving distance of the pixel along the x-axis, is the moving distance of the pixel along the y-axis;

[0090] Based on the optical flow field, motion compensation is performed on non-key frames, and the motion compensation expression is:

[0091]

[0092] where, is the image after motion compensation for the m-th frame, is the unprocessed image of the m-th frame;

[0093] Furthermore, the images after motion compensation are fused, and the fusion function expression is:

[0094]

[0095] where, is the fusion function, which combines the key frame and the motion compensation frame, is the key image of the k-th frame after equalization processing, and is the control factor, is the clarity index, which is used to quantify the detail clarity of an image. A larger value means the image is clearer, is the attenuation factor. The introduction of this parameter can help the model understand how close or well-matched the motion compensation frame is to the key frame;

[0096] The fusion function is accumulated to generate the final fused image, and the accumulation expression is:

[0097]

[0098] where, is the finally obtained fused image, is the motion compensation frame corresponding to the key frame k, which represents the image of the m-th frame after motion compensation processing.

[0099] It should be noted that the optical flow field between adjacent key frames is estimated by the long-distance image block matching algorithm to achieve accurate motion compensation, ensuring the accuracy of non-key frames in spatio-temporal consistency. The design of the fusion function takes into account the weight of the key frame, the clarity index, and the attenuation factor. This multi-factor fusion method aims to maximize the detail retention and overall smoothness of the image. The finally generated fused image is therefore more visually coherent and clear.

[0100] S4. Perform image edge enhancement on the fused image and display the image after edge enhancement to the user for real-time preview.

[0101] Furthermore, use bilateral filtering to preprocess the fused image. The bilateral filtering expression is:

[0102]

[0103] where, is the new pixel value of the pixel point after bilateral filtering processing, is the original pixel value in the fused image at the coordinate , the spatial weight function and the range weight function are used to determine the influence degree of the pixel neighborhood in the filtering, is the distance between the abscissa of the pixel position s in the neighborhood and the center point x. s and t respectively represent the position coordinates of the pixels in this neighborhood, is a neighborhood;

[0104] Apply an edge detection algorithm to the image after bilateral filtering processing to enhance the edges.

[0105] It should be noted that edge enhancement is a key step in improving the visual effect of the image, especially for scenes with rich details. First, use bilateral filtering for preprocessing, which is an effective method to remove noise while preserving edges because it takes into account both spatial proximity and pixel value similarity. Then, further strengthen the edges through edge detection algorithms such as the Sobel operator, Canny edge detection and other methods not explicitly mentioned, making the contour of the final image clearer and the details more abundant. This not only improves the visual effect of the image but also provides a better basis for subsequent analysis.

[0106] S5. The specific steps of the edge detection algorithm.

[0107] Further smooth the image to reduce noise interference;

[0108] Calculate the gradient magnitude G and direction of each pixel of the image ;

[0109] Only retain the edges in the direction of the local maximum gradient;

[0110] Dynamically adjust the threshold according to the average brightness and contrast of the image to complete the edge-enhanced image;

[0111]

[0112] where, is the high threshold, is the low threshold, is the average brightness of the image, which is used to dynamically adjust the threshold, the adjustment factor and is the difference factor, which is used to affect the adjustment of the threshold to ensure effective edge detection in images with different contrasts, is the image contrast, k and are constants, which are used to distinguish strong edges and weak edges;

[0113] Finally, the image after edge enhancement processing is rendered in real time and displayed to the user for preview through the display screen of the motion camera.

[0114] It should be noted that in the refinement step of edge detection, random noise in the image is reduced through smoothing processing, and then the gradient magnitude and direction are calculated, and the edges in the local maximum gradient direction are retained, which is the core of accurate edge detection. The strategy of dynamically adjusting the threshold ensures that appropriate edge enhancement effects can be obtained for images under different lighting and contrast conditions. Finally, the threshold is determined according to the specific features of the image, such as average brightness, contrast, etc., so that the edge enhancement is neither overly sensitive nor misses key details, ensuring that the preview image is both clear and natural, providing a high-quality preview experience for the user.

[0115] This embodiment also provides an image enhancement system during the shooting process of a motion camera, including an environmental perception and motion monitoring module, an adaptive weight calculation module, an image processing optimization module, and a real-time preview module. The environmental perception and motion monitoring module is used to collect current environmental light data and motion state data through the integrated environmental light sensor and motion detection module in the motion camera; the adaptive weight calculation module is used to perform adaptive frame rate weight calculation on the environmental light data and motion state data to generate a set of key frame weights; the image processing optimization module is used to perform histogram equalization algorithm on the set of key frame weights to obtain an equalized set of key frames, and use motion compensation technology to perform key frame motion compensation fusion processing on the equalized set of key frames to form a fused image; the real-time preview module is used to display the image after image edge enhancement to the user for preview in real time.

[0116] This embodiment also provides a computer device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the image enhancement method during the shooting process of a motion camera as proposed in the above embodiment.

[0117] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, carrier networks, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad set on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0118] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the image enhancement method during the shooting process of a sports camera as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read-Only Memory (EPROM for short), Programmable Read-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0119] In summary, through the integration of ambient light adaptability frame rate weight calculation, histogram equalization key frame optimization, dynamic motion compensation fusion, intelligent edge enhancement, and real-time preview technology, the present invention constructs an image enhancement system during the shooting process of a sports camera. This system not only effectively solves the problem of image degradation in complex lighting changes and intense motion scenes, but also significantly improves the visual quality and smoothness of the video. This system is commonly used in multiple fields such as outdoor sports photography, surveillance recording, and drone aerial photography, providing users with a highly clear, smooth, and detail-rich image display.

[0120] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An image enhancement method during the shooting process of an action camera, characterized in that: Including: The motion camera collects the current environmental light data and motion state data; Perform adaptive frame rate weight calculation on the environmental light data and motion state data to generate a set of key frame weights. The specific steps are as follows: Perform adaptive frame rate weight calculation on each frame k in the sequence of environmental light data and motion state data. The expression for adaptive frame rate weight calculation is: Among them, is the weight value of the th frame, is the size of the window, is the inverse function of the hyperbolic sine function, is the absolute value of the illumination difference between the jth frame and the reference frame, is the absolute value of the illumination difference between the kth frame and the reference frame, is a scalar coefficient, is a control factor, is the motion intensity index of the jth frame; Collect the weight values calculated for each frame to form a key frame weight set V. The expression for the key frame weight set V is as follows: ; Perform histogram equalization algorithm on the set of key frame weights to obtain an equalized set of key frames; Use motion compensation technology to perform key frame motion compensation fusion processing on the equalized set of key frames to form a fused image; Perform image edge enhancement on the fused image and display the image after image edge enhancement to the user for real-time preview.

2. The method for image enhancement during the shooting of an action camera according to claim 1, characterized in that: The environmental light data and motion state data include: light intensity, color information, directivity information, light distribution, position data, rotation data, acceleration, timestamp, motion trajectory, zoom information.

3. The method for image enhancement during the shooting of a sports camera according to claim 2, characterized in that: Perform histogram equalization algorithm on the set of key frame weights to obtain an equalized set of key frames. The specific steps are as follows: Normalize all elements in the weight set The normalization processing expression is: Among them, is the weight value of the th frame after normalization; Use the normalized weight values to construct a cumulative distribution function. The expression for the cumulative distribution function is: Among them, is the result of accumulating the first k normalized weight values, where k is an index variable and i is a loop variable; Calculate the weight value of each normalized frame and the corresponding cumulative probability , where ; Based on the cumulative probability , the equalized weights are obtained by linear interpolation ; For the equalized weight values Inverse normalize back to the scale range of the weight values, and summarize to form the equalized key-frame weight set: Among them, is the set of key frame weights for equalization.

4. The method for image enhancement during the shooting of a sports camera according to claim 3, characterized in that: The step of using motion compensation technology to perform key frame motion compensation fusion processing on the equalized set of key frames to form a fused image is as follows: For each pair of adjacent key frames in the equalized key frame weight set , the long-distance image block matching algorithm is used to estimate the optical flow field between them. The expression of the optical flow field is as follows: Among them, is the optical flow field function from the k-th frame to the (k + 1)-th frame, is the position of the pixel in the image, is the moving distance of the pixel along the x-axis, is the moving distance of the pixel along the y-axis; Based on the optical flow field, perform motion compensation on non-key frames. The expression for motion compensation is: Among them, is the image after motion compensation for the m-th frame, is the unprocessed image of the m-th frame; Fuse the images after motion compensation. The expression for the fusion function is: Among them, is the fusion function, is the k-th key image after equalization processing, and are control factors, is the clarity index, is the attenuation factor; Perform accumulation on the fusion function to generate the final fused image. The accumulation expression is: Among them, is the finally obtained fused image, is the motion compensation frame corresponding to the key frame k.

5. The method for image enhancement during the shooting of an action camera according to claim 4, characterized in that: The step of performing image edge enhancement on the fused image and displaying the image after image edge enhancement to the user for real-time preview is as follows: Use bilateral filtering to preprocess the fused image. The expression for bilateral filtering is: where, is the new pixel value of the pixel point after bilateral filtering processing, is the original pixel value at the coordinate in the fused image, is the spatial weight function, is the range weight function, is the distance between the abscissa of the pixel position s in the neighborhood and the center point x, and s and t respectively represent the position coordinates of the pixels in this neighborhood, is a neighborhood; The image after bilateral filtering processing Apply an edge detection algorithm to enhance the edges.

6. The method for image enhancement during the shooting process of an action camera according to claim 5, wherein: The specific steps of the edge detection algorithm are: Further smooth the image to reduce noise interference; Calculated image Gradient magnitude G and direction of each pixel ; Only retain the edges in the direction of the local maximum gradient; According to the image dynamically adjust the threshold based on the average brightness and contrast of the image to complete the edge-enhanced image; Among them, is the high threshold, is the low threshold, is the average brightness of the image, is the adjustment factor, is the image contrast, is the difference factor, k and are constants used to distinguish strong edges and weak edges; Finally, render the image after edge enhancement processing in real time and display it to the user for preview through the display screen of the motion camera.

7. An image enhancement system during the shooting process of an action camera, characterized in that: Including an environmental perception and motion monitoring module, an adaptive weight calculation module, an image processing optimization module, and a real-time preview module; The environmental perception and motion monitoring module is used to collect the current environmental light data and motion state data through the integrated environmental light sensor and motion monitoring module in the motion camera; The adaptive weight calculation module is used to perform adaptive frame rate weight calculation on the environmental light data and motion state data to generate a set of key frame weights. The specific steps are as follows: Perform adaptive frame rate weight calculation on each frame k in the sequence of environmental light data and motion state data. The expression for adaptive frame rate weight calculation is: Among them, is the weight value of the th frame, is the size of the window, is the inverse function of the hyperbolic sine function, is the absolute value of the illumination difference between the jth frame and the reference frame, is the absolute value of the illumination difference between the kth frame and the reference frame, is a scalar coefficient, is a control factor, is the motion intensity index of the jth frame; Collect the weight values calculated for each frame to form a key frame weight set V. The expression for the key frame weight set V is as follows: ; The image processing optimization module is used to perform histogram equalization algorithm on the set of key frame weights to obtain an equalized set of key frames, and use motion compensation technology to perform key frame motion compensation fusion processing on the equalized set of key frames to form a fused image; The real-time preview module is used to display the image after image edge enhancement to the user for real-time preview.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the image enhancement method during the shooting process of the motion camera according to any one of claims 1 to 6.

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