Vignetting correction method of panoramic camera

Abstract

The invention provides a vignetting correction method and device of a panoramic camera and a storage medium. According to the method, a target correction table corresponding to a current image color temperature of an image to be processed is selected according to the current image color temperature, a first gain value is extracted from the target correction table through a pixel corresponding relation, and initial brightness correction data is provided for each pixel; applying the first gain value in the linear RGB space, adjusting the correction intensity in combination with the pixel brightness, and carrying out the initial correction of the to-be-processed image, so as to obtain more uniform brightness distribution; and for the image edge pixel whose distance to the image center is greater than the threshold radius, calculating a second gain value and executing secondary correction to further balance the brightness of the edge and the central area, the generated secondary correction image effectively improves the brightness of the edge area while maintaining the brightness of the image center, and the image quality is improved. Therefore, the vignetting correction effectiveness of the panoramic camera is improved, the vignetting phenomenon is remarkably reduced, and the overall visual quality and consistency of the panoramic image are improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to a method for vignetting correction of a panoramic camera. Background Technology

[0002] Panoramic cameras typically use fisheye lenses to achieve wide-angle shooting effects. However, due to the complex coating process of fisheye lenses, the light transmittance at the center of the lens is higher than at the edges, resulting in darker images at the edges—a phenomenon known as vignetting. Furthermore, the ratio of light transmittance at the center to the edges varies for different wavelengths of light, causing vignetting to manifest differently at different color temperatures.

[0003] In existing technologies, module manufacturers typically calibrate vignetting correction tables during the production process. However, due to limitations in their calibration algorithms, the correction results are often unsatisfactory, leading to problems such as insufficient brightness at image edges or overexposure. Therefore, improving the effectiveness of vignetting correction in panoramic cameras has become an urgent technical problem to be solved. Summary of the Invention

[0004] This application provides a method for vignetting correction of panoramic cameras, aiming to solve the technical problems of poor vignetting correction effect, insufficient brightness at image edges or overexposure caused by the limitations of calibration algorithms in related technologies, so as to improve the effectiveness of vignetting correction of panoramic cameras.

[0005] In a first aspect, this application provides a method for vignetting correction of a panoramic camera, the method comprising the following steps: Based on the current color temperature of the image to be processed, a target correction table corresponding to the current color temperature is determined; Based on the pixel correspondence between the image to be processed and the target correction table, the first gain value corresponding to each pixel in the image to be processed is read from the target correction table; The target correction table is applied in the linear RGB space to correct the pixel brightness of each pixel in the image to be processed according to the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, so as to obtain the initial corrected image. For image edge pixels in the initial corrected image whose distance from the image center is greater than a threshold radius, a second gain value corresponding to each image edge pixel is determined based on the pixel brightness of the image center and the pixel brightness of the image edge pixels. Based on the second gain value corresponding to each of the image edge pixels, a secondary correction is performed on each of the image edge pixels so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, thereby obtaining a secondary corrected image.

[0006] This application provides a method, apparatus, and storage medium for vignetting correction of a panoramic camera. The method selects a target correction table corresponding to the current color temperature of the image to be processed, ensuring the correction process matches the lighting conditions. A first gain value is extracted from the target correction table through pixel correspondence, providing initial brightness correction data for each pixel. The first gain value is applied in the linear RGB space, and the correction intensity is adjusted in conjunction with pixel brightness to perform an initial correction on the image to be processed, achieving a more uniform brightness distribution. For image edge pixels whose distance from the image center is greater than a threshold radius, a second gain value is calculated and a secondary correction is performed to further balance the brightness of the edge and center regions. The generated secondary-corrected image effectively improves the brightness of the edge regions while maintaining the brightness of the image center, thereby improving the effectiveness of vignetting correction for the panoramic camera, significantly reducing vignetting, and improving the overall visual quality and consistency of the panoramic image. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic flowchart illustrating an embodiment of a vignetting correction method for a panoramic camera provided in this application; Figure 2 This application provides an embodiment of a raw RAW image captured by a panoramic camera using a fisheye lens; Figure 3 A correction effect diagram of initial vignetting correction using a low-resolution correction table, provided as an embodiment of this application; Figure 4 This is a flowchart illustrating an embodiment of the calibration process of a correction table in a vignetting correction method for a panoramic camera provided in this application. Figure 5 This is a schematic diagram of the structure of a first embodiment of a vignetting correction device for a panoramic camera provided in this application; Figure 6 This is a schematic block diagram of the structure of a computer device provided in an embodiment of this application.

[0009] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0011] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0012] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0013] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of a vignetting correction method for a panoramic camera provided in this application.

[0014] Generally, the conventional technique for vignetting correction in panoramic cameras is to perform vignetting correction internally within the camera, completing the correction in RAW space before writing the image or video. However, performing vignetting correction within the camera's ISP module is limited by chip computing power, making it impossible to optimize the gain value processing of the correction table or to interpolate the correction table based on color temperature.

[0015] This embodiment performs vignetting correction within the editing software on the terminal device (such as a mobile phone or PC). This leverages the computing power of these devices to flexibly process correction tables and improve the correction effect. Furthermore, performing vignetting correction in the in-camera RAW format brightens noise, increasing the processing difficulty for the subsequent noise reduction module in the ISP and leading to increased noise. This embodiment avoids this problem by performing vignetting correction post-processing.

[0016] like Figure 1 As shown, the vignetting correction method for the panoramic camera includes steps S11 to S15.

[0017] S11. Based on the current color temperature of the image to be processed, determine the target correction table corresponding to the current color temperature of the image; The image to be processed is an image like Figure 2Before performing vignetting correction on the raw image shown, it is necessary to first read or calculate the current color temperature of the image to be processed. The current color temperature can be automatically read or calculated by image analysis software, or obtained through the camera's built-in function. Image color temperature is a physical quantity that describes the color of a light source, usually expressed in Kelvin (K).

[0018] Specifically, one or more regions are selected in the image to be processed for color temperature analysis. These regions should be representative of the overall lighting conditions and are typically neutral regions in the image (such as gray or white areas). Color information, such as RGB (red, green, and blue) color values, is extracted from the selected regions, and the extracted color information is used to calculate the current color temperature of the image to be processed.

[0019] A pre-built lookup table can be used to map color values ​​to color temperature values. By comparing the color values ​​in an image with the data in the lookup table, the current color temperature of the image to be processed can be estimated. Alternatively, RGB color values ​​can be converted to other color spaces, and then the data in those color spaces can be used to calculate the current color temperature of the image to be processed. Color temperature can also be calculated directly from RGB values ​​using mathematical algorithms; for example, the proportion of red and blue components in an image can be analyzed to estimate the current color temperature of the image to be processed.

[0020] After a camera captures a picture or video, the editing software needs to select a suitable target correction table based on the current color temperature of the image to be processed. For video sequences, the color temperature of each frame in the video needs to be read individually and a target correction table needs to be selected.

[0021] In one embodiment, the calibration tables are pre-calibrated, and multiple preset calibration tables corresponding to different image color temperatures are stored in the camera device's registers. Generally, the preset calibration tables are generated at specific color temperatures, such as 2854K, 5000K, and 6500K. These color temperature values ​​represent different lighting conditions, such as warm light, neutral light, and cool light. In practical applications, a target calibration table corresponding to the current image color temperature can be read from the register based on the current image color temperature of the image to be processed, and used for vignetting correction of the image to be processed.

[0022] In one embodiment, the preset calibration table includes a first calibration table corresponding to a first image color temperature and a second calibration table corresponding to a second image color temperature, wherein the first image color temperature is lower than the second image color temperature. When the current image color temperature is lower than or equal to the first image color temperature, the first calibration table is determined as the target calibration table; when the current image color temperature is higher than or equal to the second image color temperature, the second calibration table is determined as the target calibration table.

[0023] The first calibration table corresponds to a lower first image color temperature, and the second calibration table corresponds to a higher second image color temperature. First, the current image color temperature of the image to be processed is read using image processing software or hardware.

[0024] Compare the color temperature of the current image, the color temperature of the first image, and the color temperature of the second image. If the current image color temperature... If the color temperature is lower than or equal to the first image color temperature, then the first correction table corresponding to the first image color temperature is directly selected as the target correction table; if the current image color temperature is lower than or equal to the first image color temperature, then the first correction table corresponding to the first image color temperature is selected as the target correction table. If the color temperature is higher than or equal to the second image color temperature, then the second correction table corresponding to the second image color temperature is directly selected as the target correction table.

[0025] For example, suppose the color temperature of the first image is 2854K and the color temperature of the second image is 6500K. If the current color temperature of the image to be processed is read as 2400K, which is lower than 2854K (i.e., lower than the color temperature of the first image), then the correction table corresponding to 2854K is applied to the image to be processed. If the current color temperature of the image to be processed is read as 6700K, which is higher than 6500K (i.e., higher than the color temperature of the second image), then the correction table corresponding to 6500K is applied to the image to be processed.

[0026] In one embodiment, the preset calibration table further includes a third calibration table corresponding to a third image color temperature, wherein the third image color temperature is between the first image color temperature and the second image color temperature. When the current image color temperature is higher than the first image color temperature and lower than the second image color temperature, the two image color temperatures closest to the current image color temperature are found among the first image color temperature, the second image color temperature, and the third image color temperature; interpolation calculation is performed on the preset calibration table corresponding to the two image color temperatures closest to the current image color temperature to generate the target calibration table.

[0027] The camera device's registers may also include a third correction table corresponding to a third image color temperature that is between the first and second image color temperatures. This third image color temperature may correspond to an intermediate color temperature, such as 5000K, thereby enabling more precise processing of images located between two extreme color temperatures (2854K and 6500K).

[0028] Similarly, the current color temperature of the image to be processed is read and compared with the corresponding image color temperature in the preset calibration table. If the current image color temperature is between the first and second image color temperatures, then the current image color temperature is further compared with the third image color temperature to find the match between the current image color temperature and the first image color temperature. The two closest color temperatures of the images, such as the color temperature of the first image and the color temperature of the third image, or the color temperature of the second image and the color temperature of the third image, are respectively... and .

[0029] Assume the current color temperature of the image to be processed is Let the color temperatures of the two images closest to the current image color temperature be denoted as . and The correction tables for the color temperatures of these two images are denoted as follows: and This corresponds to the current image color temperature. Target correction table : in, and Corresponding to and The correction table.

[0030] S12. Based on the pixel correspondence between the image to be processed and the target correction table, read the first gain value corresponding to each pixel in the image to be processed from the target correction table; After obtaining the target calibration table corresponding to the current image color temperature, it needs to be applied to the image to be processed in the editing software. Because the size of the target calibration table may be different from the size of the image to be processed, it is necessary to map the pixels in the image to be processed one-to-one with the pixels on the target calibration table, find the correct pixel correspondence through proportional conversion, and convert the pixel coordinates in the image to the coordinates on the target calibration table.

[0031] Specifically, based on the aspect ratio of the image to be processed and the target calibration table, the corresponding position of each pixel in the image to be processed on the target calibration table is calculated, and the corresponding position is represented by a floating-point number. When performing position conversion based on the pixel correspondence, the discrete pixel coordinates need to be converted into continuous coordinates of 0 to 1. Taking the top-left pixel coordinates as (0, 0) as an example, the pixel needs to be regarded as a square with an area during the conversion, and the top-left corner of the (0, 0) pixel is assigned to the continuous coordinate (0, 0).

[0032] For example, suppose the size of the image to be processed is The size of the target calibration table is For each pixel in the image to be processed The floating-point coordinates corresponding to the target calibration table are calculated using the following formula. : in, and These are the width and height of the image to be processed, respectively. These are the discrete coordinates of the pixels in the image to be processed. These are the floating-point coordinates of the pixel in the target correction table.

[0033] Since the pixel values ​​on the target calibration table are discrete, and the coordinate transformation may yield floating-point positions, bilinear interpolation is needed to obtain a precise gain value, i.e., the first gain value. The first gain value calculated from the target calibration table for the corresponding position is used to adjust the brightness of the corresponding pixel in the image to be processed.

[0034] Specifically, based on the calculated floating-point coordinates The gain value is obtained from the four nearest pixels in the target correction table, and the final first gain value is obtained by bilinear interpolation. : in, and It is based on floating-point coordinates The calculated interpolation weights, , , and It is the target calibration table and the floating point coordinates The gain value of the four neighboring pixels.

[0035] S13. Apply the target correction table in the linear RGB space to correct the pixel brightness of each pixel in the image to be processed according to the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, and obtain the initial corrected image. Pixel brightness correction is performed by applying a target correction table in the linear RGB space, and the calculated first gain value is then used. Brightness adjustment applied to corresponding pixels in the image to correct vignetting. It aims to adjust image brightness based on the gain value and correction intensity of each pixel to improve image quality.

[0036] Specifically, the image to be processed is converted from its original color space (such as YUV or sRGB) to a linear RGB color space. This is because most image sensor output RAW image data requires color space conversion before effective brightness correction can be performed. Using a first gain value obtained from the target correction table, brightness adjustment is applied to each pixel in the linear RGB space; this first gain value reflects the amount of brightness increase needed for each pixel. The correction intensity is calculated based on the pixel's brightness to avoid overexposure and optimize the correction effect; the correction intensity is typically inversely proportional to the pixel's current brightness. The gain value and correction intensity are applied to each pixel for brightness correction, generating the initial corrected image.

[0037] For RAW images, since their color space is linear RGB, the target correction table can be used directly. However, for JPG or video images, since their YUV color space is no longer linear, they need to be converted to a linear RGB color space before applying the target correction table. After the image is corrected, it is then converted back to the YUV color space.

[0038] The YUV color space is a color coding method widely used in video and broadcasting. It separates the color information of an image into two components: luminance (Y) and chrominance (U and V). This separation helps compress image data while maintaining good visual quality.

[0039] First, the YUV color space needs to be converted to the RGB color space according to the specific color space standard used by the image material (such as BT.709 or BT.2020). This step usually involves a fixed conversion matrix that defines how to calculate the RGB values ​​from the YUV values. For example, for the BT.709 standard, the conversion formula might be as follows: Since most images and videos are captured and stored with gamma correction applied (to simulate the non-linear perception of brightness by the human eye), after converting the image from YUV to RGB, an inverse gamma curve needs to be applied to convert the RGB values ​​to the linear RGB space.

[0040] The inverse gamma curve is a lookup table (LUT) that defines how to convert non-linear RGB values ​​into linear values. This table is typically generated by the image sensor or ISP (Image Signal Processor) and is 1024 in length (corresponding to 256 possible values ​​for each channel of an 8-bit image). Before applying the inverse gamma curve, the RGB values ​​need to be normalized to the range of 0 to 1. This is because gamma correction is usually defined in the range of 0 to 255, while the inverse gamma curve is defined in the range of 0 to 1.

[0041] After applying the target correction table and making necessary brightness adjustments to the image, the linear RGB values ​​are converted back to the RGB color space using a positive gamma curve to ensure correct image display on the display device. This process involves applying a positive gamma curve, which defines how to convert linear RGB values ​​back to non-linear RGB values. This is also a lookup table used to map linear values ​​back to their original non-linear range.

[0042] This embodiment converts the image from the YUV color space to the linear RGB space, applies a calibration table to adjust the brightness, and then converts it back to the RGB color space to ensure that the color and brightness of the image remain consistent under different devices and display conditions.

[0043] When applying a correction table in linear RGB space, it is common practice to directly multiply each pixel value by the corresponding gain value. However, this method may cause areas in the image that are already close to their maximum brightness value (i.e., overexposed) to further increase in brightness, exceeding the representable range and resulting in overexposure. To avoid this, this application adjusts the first gain value based on the pixel brightness to prevent the increase of overexposed areas.

[0044] In one embodiment, the maximum brightness value corresponding to each pixel is obtained; based on the maximum brightness value, the pixel correction intensity corresponding to each pixel is calculated.

[0045] First, for each pixel, determine the maximum luminance value among its three RGB channels. This value represents the brightest part of the pixel in the three color channels and is an important indicator for assessing whether the pixel is close to overexposure.

[0046] Then, according to Calculate the correction strength Correction strength It is a value between 0 and 1, used to adjust the degree of gain application. The calculation formula is: This formula ensures that the darker the pixel (... A value close to 0 will result in a larger correction strength. (Closer to 1), thus allowing these pixels to receive more brightness boost. Conversely, the closer the brightness is to overexposed pixels ( Approaching 1) will result in a smaller correction strength ( (Approaching 0), thereby reducing the over-application of gain values ​​and avoiding overexposure.

[0047] After calculating the correction strength Then, a first gain value can be applied to adjust the brightness of each pixel in the image to be processed. Specifically, based on the pixel correction intensity, the first gain value corresponding to each pixel in the image to be processed is adjusted, and the corrected pixel brightness corresponding to each pixel is calculated; the pixel brightness of each pixel in the image to be processed is corrected to the corresponding corrected pixel brightness to obtain the initial corrected image.

[0048] The formula for calculating the corrected pixel brightness is as follows: in, It is the original pixel brightness. It is the first gain value obtained from the calibration table. The pixel correction intensity, This is the corrected pixel brightness.

[0049] This formula modifies the first gain value with the correction strength. Multiplication enables dynamic adjustment of the first gain value. For pixels with lower brightness, The first gain value is close to 1, therefore it is close to 1. For pixels with higher brightness, With the gain value close to 0, the first gain value is close to 1, thus reducing the risk of overexposure.

[0050] This embodiment dynamically adjusts the application degree of the first gain value based on pixel brightness, effectively avoiding overexposure while improving the overall brightness and contrast of the image to be processed. Even after applying the first gain value, areas in the image that are already close to maximum brightness will not be over-enhanced, thus effectively preventing overexposure. This is particularly important for processing images with complex brightness distributions, such as high dynamic range (HDR) images, ensuring that the image to be processed maintains high-quality visual effects under different brightness conditions.

[0051] Furthermore, some camera devices have small module registers, resulting in low-resolution preset correction tables. Since vignetting worsens towards the edges, using a low-resolution correction table (e.g., 17×17 pixels) for vignetting correction fails to provide sufficient detail for image edges. This leads to a large area at the very edge being represented by the average gain value of that area, resulting in insufficient gain in the edge regions and inadequate vignetting correction. Therefore, for target correction tables with very low resolution (e.g., 17×17), secondary correction is required during post-processing to brighten the gain values ​​at the edges.

[0052] S14. For image edge pixels in the initial corrected image whose distance from the image center is greater than a threshold radius, determine the second gain value corresponding to each image edge pixel based on the pixel brightness of the image center and the pixel brightness of the image edge pixels. Images captured by panoramic cameras or fisheye lenses are prone to vignetting, meaning the edges of the image are darker than the center. When using a low-resolution calibration table (such as 17×17) for correction, the calibration table cannot provide enough detail, resulting in insufficient brightness correction in the image edge areas and obvious dark edges.

[0053] like Figure 3 As shown, after correcting the initial image using a low-resolution target correction table such as 17×17, the image content covered by the pixels at the outermost edge of the fisheye of the target correction table will be noticeably darker, as indicated by the arrow. Other areas also show slight grid-like variations in brightness and darkness, but these are not visible to the naked eye in actual scenes.

[0054] To solve this problem, a threshold radius is first defined. This is used to distinguish between the central and edge regions of an image. Threshold radius The calculation formula can be expressed as: in, It is the width of the calibration table. It is the width of the image. This refers to the threshold radius, which is used to determine which pixels are located in the edge region and require secondary correction.

[0055] For each pixel, calculate its distance to the image center. Then based on the threshold radius Calculate the normalized distance : in, This represents the distance from pixel p to the center of the image. Indicates the threshold radius. Indicates the image width. This indicates the relative position of a pixel from the edge of the image.

[0056] In one embodiment, the normalized distance corresponding to the image edge pixels can be used. A polynomial model is fitted, and then the polynomial coefficients a, b, c, and d are solved using the least squares method to find a set of polynomial coefficients that make the pixel gain value predicted by the polynomial model consistent with the pixel brightness of the image edge pixels when applied to the image edge pixels.

[0057] Further, based on the distance between the image edge pixels and the image center, the normalized distance corresponding to the image edge pixels is calculated; a polynomial model architecture is constructed, and the polynomial coefficients of the polynomial model architecture are solved according to the pixel brightness of the image center, the pixel brightness of the image edge pixels, and the normalized distance to obtain the target polynomial; based on the target polynomial and the normalized distance, the second gain value corresponding to each image edge pixel is calculated.

[0058] The objective polynomial is expressed as: Where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. This is the second gain value corresponding to the edge pixels of the image.

[0059] In one embodiment, the objective polynomial needs to be applied to every image edge pixel in the image edge region, and the second gain value of each image edge pixel is solved sequentially. Therefore, in this objective polynomial, the normalized distance corresponding to each image edge pixel is different, i.e., the normalized distance is different. Since it is a variable, it is necessary to first solve for the constant values ​​of the coefficients of each polynomial, and then apply the target polynomial to solve for the second gain value corresponding to each edge pixel of the image.

[0060] In one embodiment, the polynomial model architecture is constructed; the target gain value is obtained by dividing the pixel brightness of the image center of the initially corrected image by the pixel brightness of the image edge pixels; based on the target gain value corresponding to the image edge pixels and the normalized distance, the coefficients of each polynomial are solved to obtain the target polynomial.

[0061] The polynomial model architecture is represented as follows: Where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. The target gain value is the optimal gain value that adjusts the pixel brightness of the edge pixels of the image to be consistent with the pixel brightness of the center pixels of the image.

[0062] In the initial image correction, pixel regions with a distance from the image center pixel coordinates less than or equal to a preset threshold radius are designated as the image center region, while pixel regions with a distance greater than the threshold radius are designated as the image edge region. The threshold radius can be dynamically set based on pixel brightness.

[0063] After applying the target correction table for pixel brightness correction, a brightness distribution map of the image can be calculated for brightness analysis. This map shows the distribution of different brightness values ​​within the image. Based on the brightness analysis results, areas with good brightness correction are designated as the image center region, including the central portion of the image and surrounding areas with brightness close to the center. Areas with poor brightness correction, i.e., those with significant differences in brightness values ​​compared to the center, are designated as image edge regions. These areas are typically located at the edges of the image, primarily due to lens distortion or uneven lighting, leading to more severe vignetting.

[0064] The threshold radius can be determined based on the brightness difference between the image's central and edge regions. One approach is to calculate the average brightness of the central and edge regions and then dynamically adjust the threshold radius based on the difference between these two values. Another approach is to use the image's histogram to determine the inflection point of brightness change, using the brightness value corresponding to this inflection point as the dividing line.

[0065] Specifically, the brightness distribution of the initially calibrated image is calculated. Based on this distribution, regions with brightness close to the center brightness are identified as the image center region, while regions with significant brightness differences are identified as image edge regions. The threshold radius is dynamically adjusted based on the brightness difference between the image center and edge regions. Specifically, a brightness threshold (e.g., a certain percentage of the center brightness) can be set, with regions brighter than this threshold considered as the image center region and regions brighter than this threshold considered as image edge regions.

[0066] Different correction strategies are applied to the central and edge regions. The central region of the image does not require secondary correction, while the edge regions require a larger gain value for secondary correction.

[0067] After dividing the initially corrected image into a central region and an edge region, the target gain value is obtained by dividing the pixel brightness of the central pixel by the pixel brightness of the edge pixel. in, It is the pixel brightness at the center of the image. It represents the brightness of pixels at the image edges. The target gain value is the optimal gain that adjusts the brightness of pixels at the image edges to match the brightness of pixels at the image center. By using this target gain value to perform secondary correction on the brightness of pixels at the image edges, the brightness of pixels at the image edges can be adjusted to match the brightness of pixels at the image center.

[0068] For the image edge regions in the initial correction image, you can first select a number of image edge pixels and calculate their normalized distance. and the corresponding target gain value This is used to solve for the polynomial coefficients of a polynomial model. The least squares method is used to solve for the polynomial coefficients a, b, c, and d.

[0069] Specifically, a matrix can be constructed. Each row corresponds to a data point of one image edge pixel, containing , , ,and Value: Construct a target vector The target gain value includes all edge pixels: Solving for polynomial coefficients using normal equations: in, It is a vector containing the polynomial coefficients a, b, c, and d. yes transpose, yes The inverse matrix.

[0070] After solving for the polynomial coefficients a, b, c, and d, the target polynomial can be obtained. This is applied to each image edge pixel in the image edge region of the initially calibrated image. The normalized distance corresponding to each image edge pixel is calculated. Then, the second gain value corresponding to each image edge pixel is calculated through the objective polynomial. The gain of the image edge pixel is then adjusted so that its brightness is consistent with the brightness of the pixel in the center of the image.

[0071] S15. Based on the second gain value corresponding to each of the image edge pixels, perform secondary correction on each of the image edge pixels so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, and obtain a secondary corrected image.

[0072] After determining the polynomial coefficients, the polynomial model can be used to calculate the second gain value corresponding to each image edge pixel. Then, a secondary correction is applied to the image edge pixels based on the second gain value, that is, the brightness of each image edge pixel is adjusted using the aforementioned polynomial function, so that the adjusted pixel brightness of each image edge pixel is achieved. The brightness is consistent with the pixel brightness at the center of the image.

[0073] Further, the original pixel brightness corresponding to each image edge pixel in the initial corrected image is obtained; based on the second gain value calculated by the target polynomial and the original pixel brightness, the target pixel brightness corresponding to each image edge pixel is calculated; the pixel brightness of each image edge pixel is adjusted to the corresponding target pixel brightness, so that the pixel brightness of the image edge pixel is consistent with the brightness of the image center, and a secondary corrected image is obtained. Specifically, the initial calibration image can be converted into a grayscale image or a luminance image, the original pixel luminance of each image edge pixel in the initial calibration image can be read, and the normalized distance between each image edge pixel and the image center can be calculated according to the aforementioned method.

[0074] After obtaining the target polynomial, the corrected pixel brightness of each image edge pixel is calculated using the following formula for calculating the target pixel brightness: in, It is the original pixel brightness of the pixels at the image edge. This represents the target pixel brightness after adjusting the image edge pixels, where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. This is the second gain value corresponding to the edge pixels of the image.

[0075] The calculated target pixel brightness is applied to the corresponding image edge pixels. Each image edge pixel is traversed, and its pixel brightness is adjusted to the target pixel brightness. All the adjusted pixels are combined into a new image, which is the secondary correction image.

[0076] Generally, after applying a gain value, it is necessary to ensure that the brightness value does not exceed the maximum value of the image data type (e.g., the maximum brightness value for an 8-bit image is 255). This can be achieved by limiting the adjusted brightness value within a valid range: in, It is the maximum brightness value allowed by the image data type.

[0077] This embodiment provides a vignetting correction method for panoramic cameras. The method selects a target correction table corresponding to the current color temperature of the image to be processed, ensuring the correction process matches the lighting conditions. A first gain value is extracted from the target correction table through pixel correspondence, providing initial brightness correction data for each pixel. The first gain value is applied in the linear RGB space, and the correction intensity is adjusted in conjunction with pixel brightness to perform an initial correction on the image to be processed, achieving a more uniform brightness distribution. For image edge pixels whose distance from the image center is greater than a threshold radius, a second gain value is calculated and a secondary correction is performed to further balance the brightness of the edge and center regions. The generated secondary-corrected image effectively improves the brightness of the edge regions while maintaining the brightness of the image center, thereby improving the effectiveness of vignetting correction for panoramic cameras, significantly reducing vignetting, and improving the overall visual quality and consistency of panoramic images.

[0078] Please refer to Figure 4 , Figure 4 This is a schematic flowchart illustrating an embodiment of a calibration table in a panoramic camera vignetting correction method provided in this application.

[0079] In this embodiment, as Figure 4 As shown, based on the above Figure 1 In the illustrated embodiment, before step S11, the following steps are also included: S21. For the original RAW image acquired at the preset image color temperature, extract the RGGB four-channel pixels to obtain four single-channel RAW images. In one embodiment, the calibration of the calibration table needs to be performed at multiple different preset image color temperatures. Images are taken using a uniform light source at each preset image color temperature (e.g., 2854K, 5000K, and 6500K). Figure 2 The RAW images shown are taken at different preset image color temperatures and used as input to the calibration algorithm to generate a calibration table for different image color temperatures.

[0080] Specifically, taking 2854K, 5000K, and 6500K preset image color temperatures as examples, prepare uniform light sources for these three different color temperatures. Place the panoramic camera in the light source environment, aligning the center of the lens with the center of the light source to avoid uneven lighting affecting the shooting effect. Shoot RAW images at each of the three preset image color temperatures: 2854K, 5000K, and 6500K. Multiple images can be taken at each preset image color temperature to ensure the stability and reliability of the image data. Save the captured RAW images as input files for subsequent calibration algorithms.

[0081] The calibration algorithm takes a RAW image as input, which is read from the input file of the calibration algorithm. The RAW image is captured at a preset color temperature. Based on the Bayer pattern arrangement of the RAW image, such as RGGB, the pixels in the RAW image are separated according to color channels. Each pixel is assigned to a corresponding R, G1, G2, or B channel. For each color channel, a new single-channel RAW image is generated, resulting in four single-channel RAW images, each with half the width and height. For example, for a 2000×2000 pixel RAW image, each extracted single-channel RAW image will be 1000×1000 pixels in size.

[0082] Save the four single-channel RAW images as separate data structures or files for subsequent processing.

[0083] S22. Calculate the third gain value of each pixel in each single-channel RAW image; The input to the calibration algorithm is a sheet of paper such as Figure 2The RAW image shown. Based on the Bayer Pattern of the RAW image, the pixels of the four channels RGGB are extracted. Then, for each single-channel RAW image, the average brightness of the central fixed-size pixel region is calculated. This average brightness value is then divided by the pixel brightness of each pixel in the single-channel RAW image to obtain the gain value of each pixel, i.e., the third gain value. That is, each pixel should have its pixel brightness multiplied by this third gain value to be corrected to the standard brightness.

[0084] Further, a second pixel region of a preset pixel size is extracted from the central region of each single-channel RAW image; based on the pixel brightness of each pixel in the second pixel region and the total number of pixels in the second pixel region, the average brightness of each single-channel RAW image is calculated; for each single-channel RAW image, the average brightness is divided by the pixel brightness of each pixel in the single-channel RAW image to obtain the third gain value of each pixel in the single-channel RAW image.

[0085] A pixel region of a preset pixel size, called the second pixel region, can be extracted from the central region of each single-channel RAW image for calculating average brightness. This second pixel region can be a square region, and its preset pixel size can be set according to actual needs, such as 40×40 pixels. This preset pixel size needs to be large enough to contain enough pixels for statistics, but not too large to avoid including too much edge information.

[0086] Specifically, the center point of each single-channel RAW image is determined. For a single-channel RAW image with width W and height H, the coordinates of the center point are: Select the preset pixel size for the second pixel region according to your needs, for example, 40×40 pixels. Extract a square region centered on the center point. For example, for a 40×40 pixel region, its top-left corner coordinates are... The coordinates of the lower right corner are .

[0087] Calculate the average brightness of all pixels within the second pixel region. The average brightness is obtained by summing the brightness values ​​of all pixels within the region and then dividing by the total number of pixels. Specifically, a variable can be initialized (e.g., ...). This variable is used to accumulate pixel brightness values ​​and is initialized to 0. It iterates through all pixels within the second pixel region, reading the brightness value of each pixel and accumulating it into the variable. In the middle. Determine the total number of pixels within the second pixel region. For a 40×40 pixel area, the total number of pixels is 40×40=1600. The average brightness of the second pixel area is calculated based on the total number of pixels and the accumulated brightness value. in, This represents the average brightness of the second pixel area. This represents the cumulative brightness value of all pixels within the second pixel region. This represents the total number of pixels within the second pixel region.

[0088] For each pixel in each single-channel RAW image, the gain value (third gain value) of that pixel is obtained by dividing the average brightness of the second pixel region by the brightness value of that pixel. This gain value represents the factor by which the pixel needs to be multiplied to bring its brightness to the average brightness level.

[0089] Specifically, a gain map of the same size as the single-channel RAW image can be created to store the third gain value for each pixel. Then, iterate through each pixel in the single-channel RAW image. Read its brightness value The third gain value for each pixel is calculated using the following formula: in, Represents pixels The corresponding third gain value, This represents the average brightness of the second pixel region. Represents pixels The corresponding brightness value. The calculated gain value. Stored in the corresponding location of the gain map. .

[0090] S23. According to the width and height dimensions of the preset calibration table, each single-channel RAW image is divided into at least one first pixel region; Based on the size limitations of the register, determine the width and height dimensions of the preset calibration table (usually not exceeding 100×100 pixels). Based on the aspect ratio of the single-channel RAW image to the preset calibration table, divide each single-channel RAW image into (width×height) first pixel regions.

[0091] For example, assuming the preset calibration table has a width and height of 50×50 pixels, for a 1000×1000 pixel single-channel RAW image, it is divided into... Each first pixel region has a width and height of 50×50 pixels.

[0092] For each pixel region, record its position and extent in the single-channel RAW image. For example, the... The coordinates of the top left corner of the pixel region are The coordinates of the lower right corner are .

[0093] S24. Based on the third gain value corresponding to each pixel in the first pixel region, calculate the average gain value of the first pixel region, and write the average gain value into the preset correction table at the position corresponding to the first pixel region to obtain the preset correction table corresponding to the preset image color temperature.

[0094] For all pixels within each first pixel region, calculate the average of their third gain values, which is then used as the average gain value for the first pixel region. The calculated average gain value is then written into the corresponding position in the preset correction table.

[0095] Specifically, for each first pixel region, all pixels within that first pixel region are traversed, and the average value of its third gain is calculated. : in, This indicates the number of pixels within the first pixel region. This represents the average gain value of the first pixel region. The coordinates of the first pixel region are... The third gain value of the pixel.

[0096] It is important to note that in images captured by fisheye panoramic cameras, some pixel areas at the edges may contain both normal images and black borders. When calculating the average gain value, these black borders need to be excluded. This embodiment uses a brightness threshold to filter out black border pixels; pixels below this threshold are not included in the average gain calculation.

[0097] Further, the pixel brightness of each pixel in the first pixel region is obtained; target pixels in the first pixel region whose pixel brightness is greater than a preset brightness threshold are selected, and the average gain value of the first pixel region is calculated based on the third gain value corresponding to each target pixel.

[0098] In one embodiment, the range of each first pixel region is determined based on the width and height dimensions of a preset calibration table. All pixels within each first pixel region are traversed, and the pixel brightness of each pixel is extracted. During the traversal, the pixel brightness of each pixel can be stored in an array or list.

[0099] Within each first pixel region, target pixels with a brightness greater than a preset brightness threshold are selected. These target pixels are considered valid pixels and used to calculate the average gain value. Specifically, a brightness threshold is set according to actual needs. For example, the brightness threshold can be set to 10, and pixels below this threshold are considered black borders or invalid pixels. All pixels within each first pixel region are traversed, and target pixels with a brightness greater than the brightness threshold are selected. The brightness value and position of the target pixels are stored in an array or list.

[0100] For each first pixel region, calculate the average of the third gain values ​​corresponding to all target pixels in the array or list. Write the calculated average gain value to the corresponding position in the preset correction table. For example, for the first pixel region... The first pixel region will Write into the preset calibration table Location.

[0101] The generated preset calibration table is written into the lens module's registers for use during actual shooting and image processing. The preset calibration table typically contains 16-bit gain values ​​for the four channels RGGB, with the average of the gain values ​​of the two G channels used as the final G channel gain value.

[0102] Specifically, the three generated preset calibration tables (corresponding to color temperatures of 2854K, 5000K, and 6500K respectively) can be converted into a format suitable for register storage, ensuring that the data format of the preset calibration tables is consistent with the requirements of the module registers. Using tools or interfaces provided by the module manufacturer, the preset calibration table data is written into the lens module's registers. During actual shooting, the corresponding preset calibration table can be selected for vignetting correction based on the color temperature of the shooting scene.

[0103] This embodiment generates preset correction tables for different color temperatures by acquiring RAW images at multiple preset color temperatures and calculating their gain values. It not only considers the average brightness of the image's central region when calculating the gain value but also eliminates interference from invalid pixels such as black borders by setting a brightness threshold, ensuring the accuracy and reliability of the gain values. The resulting preset correction tables effectively correct vignetting in images captured at different color temperatures, significantly improving overall image quality and enhancing the panoramic camera's imaging performance under various lighting conditions. Furthermore, by writing the correction tables into the lens module's registers, it enables rapid application of the correction tables in actual shooting, improving the efficiency and flexibility of image processing.

[0104] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a first embodiment of a vignetting correction device for a panoramic camera provided in this application. The vignetting correction device for the panoramic camera is used to perform the aforementioned vignetting correction method for the panoramic camera.

[0105] like Figure 5 As shown, the vignetting correction device 30 of the panoramic camera includes: a target correction table determination module 31, a first gain value reading module 32, a target correction table application module 33, a second gain value calculation module 34, and a secondary correction module 35.

[0106] The target correction table determination module 31 is used to determine a target correction table corresponding to the current image color temperature based on the current image color temperature of the image to be processed. The first gain value reading module 32 is used to read the first gain value corresponding to each pixel in the image to be processed from the target correction table based on the pixel correspondence between the image to be processed and the target correction table; The target correction table application module 33 is used to apply the target correction table in the linear RGB space to correct the pixel brightness of each pixel in the image to be processed according to the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, so as to obtain the first corrected image. The second gain value calculation module 34 is used to determine the second gain value corresponding to each image edge pixel in the initial correction image based on the pixel brightness of the image center and the pixel brightness of the image edge pixels, according to the image edge pixels whose distance from the image center is greater than the threshold radius. The secondary correction module 35 is used to perform secondary correction on each of the image edge pixels based on the second gain value corresponding to each of the image edge pixels, so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, thereby obtaining a secondary corrected image.

[0107] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the device and each module described above can be referred to the corresponding process in the aforementioned embodiment of the panoramic camera vignetting correction method, and will not be repeated here.

[0108] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 6 It runs on the computer device shown.

[0109] Please see Figure 6 , Figure 6 This is a schematic block diagram illustrating the structure of a computer device according to an embodiment of this application. The computer device may be a server.

[0110] See Figure 6 The computer device includes a processor, memory, and network interface connected via a system bus, wherein the memory may include non-volatile storage media and internal memory.

[0111] The non-volatile storage medium can store an operating system and a computer program. This computer program includes program instructions that, when executed, cause the processor to perform any vignetting correction method for a panoramic camera.

[0112] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0113] Internal memory provides an environment for the execution of computer programs in non-volatile storage media. When executed by a processor, the computer program enables the processor to perform any vignetting correction method for a panoramic camera.

[0114] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0115] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0116] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps: Based on the current color temperature of the image to be processed, a target correction table corresponding to the current color temperature is determined; Based on the pixel correspondence between the image to be processed and the target correction table, the first gain value corresponding to each pixel in the image to be processed is read from the target correction table; The target correction table is applied in the linear RGB space to correct the pixel brightness of each pixel in the image to be processed according to the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, so as to obtain the initial corrected image. For image edge pixels in the initial corrected image whose distance from the image center is greater than a threshold radius, a second gain value corresponding to each image edge pixel is determined based on the pixel brightness of the image center and the pixel brightness of the image edge pixels. Based on the second gain value corresponding to each of the image edge pixels, a secondary correction is performed on each of the image edge pixels so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, thereby obtaining a secondary corrected image.

[0117] The embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions, and the processor executing the program instructions to implement any of the panoramic camera vignetting correction methods provided in the embodiments of this application.

[0118] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiments, such as the hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the computer device.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for vignetting correction in a panoramic camera, characterized in that, The method includes: Based on the current color temperature of the image to be processed, a target correction table corresponding to the current color temperature is determined; Based on the pixel correspondence between the image to be processed and the target correction table, the first gain value corresponding to each pixel in the image to be processed is read from the target correction table; The target correction table is applied in the linear RGB space to correct the pixel brightness of each pixel in the image to be processed according to the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, so as to obtain the initial corrected image. For image edge pixels in the initial corrected image whose distance from the image center is greater than a threshold radius, a second gain value corresponding to each image edge pixel is determined based on the pixel brightness of the image center and the pixel brightness of the image edge pixels. Based on the second gain value corresponding to each of the image edge pixels, a secondary correction is performed on each of the image edge pixels so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, thereby obtaining a secondary corrected image.

2. The vignetting correction method for a panoramic camera according to claim 1, characterized in that, The step of determining the second gain value corresponding to each image edge pixel based on the pixel brightness of the image center and the pixel brightness of the image edge pixels includes: Based on the distance between the image edge pixels and the image center, calculate the normalized distance corresponding to the image edge pixels; Construct a polynomial model architecture, and solve the polynomial coefficients of the polynomial model architecture based on the pixel brightness of the image center, the pixel brightness of the image edge pixels, and the normalized distance to obtain the target polynomial; Based on the target polynomial and the normalized distance, calculate the second gain value corresponding to each of the image edge pixels; The objective polynomial is expressed as: Where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. This is the second gain value corresponding to the edge pixels of the image.

3. The vignetting correction method for a panoramic camera according to claim 2, characterized in that, The construction of the polynomial model architecture involves solving for the polynomial coefficients of the polynomial model architecture based on the pixel brightness at the image center, the pixel brightness at the image edge, and the normalized distance, to obtain the target polynomial, including: Construct the polynomial model architecture; The target gain value is obtained by dividing the pixel brightness of the image center of the initially corrected image by the pixel brightness of the image edge pixels; Based on the target gain value and the normalized distance corresponding to the image edge pixels, the coefficients of each polynomial are solved to obtain the target polynomial; The polynomial model architecture is represented as follows: Where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. This represents the target gain value.

4. The vignetting correction method for a panoramic camera according to claim 2, characterized in that, The step of performing secondary correction on each image edge pixel based on the second gain value corresponding to each image edge pixel, so that the pixel brightness of the image edge pixel is consistent with the brightness of the image center, to obtain a secondary corrected image, includes: Obtain the original pixel brightness corresponding to each image edge pixel in the initial corrected image; Based on the second gain value obtained by the target polynomial and the original pixel brightness, calculate the target pixel brightness corresponding to each of the image edge pixels; The pixel brightness of each of the image edge pixels is adjusted to the corresponding target pixel brightness, so that the pixel brightness of the image edge pixels is consistent with the brightness of the image center, thereby obtaining a secondary corrected image; The formula for calculating the brightness of the target pixel is as follows: in, It is the original pixel brightness of the pixels at the image edge. This represents the target pixel brightness after adjusting the image edge pixels, where a, b, c, and d are polynomial coefficients. The normalized distance corresponding to the edge pixels of the image. This is the second gain value corresponding to the edge pixels of the image.

5. The vignetting correction method for a panoramic camera according to claim 1, characterized in that, Before correcting the pixel brightness of each pixel in the image to be processed based on the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, and obtaining the first corrected image, the method further includes: Obtain the maximum brightness value corresponding to each pixel in the image to be processed; Based on the maximum brightness value, the pixel correction intensity corresponding to each pixel is calculated.

6. The vignetting correction method for a panoramic camera according to claim 5, characterized in that, The step of correcting the pixel brightness of each pixel in the image to be processed based on the first gain value and pixel correction intensity corresponding to each pixel in the image to be processed, to obtain an initial corrected image, includes: Based on the pixel correction intensity, the first gain value corresponding to each pixel in the image to be processed is adjusted, and the corrected pixel brightness corresponding to each pixel is calculated; The pixel brightness of each pixel in the image to be processed is corrected to the corresponding corrected pixel brightness to obtain the initial corrected image; The formula for calculating the corrected pixel brightness is as follows: in, It is the original pixel brightness. It is the first gain value obtained from the calibration table. The pixel correction intensity, This is the corrected pixel brightness.

7. The vignetting correction method for a panoramic camera according to claim 1, characterized in that, Before determining the target correction table corresponding to the current image color temperature based on the current image color temperature of the image to be processed, the method further includes: For the original RAW image acquired at a preset image color temperature, extract the RGGB four-channel pixels to obtain four single-channel RAW images; Calculate the third gain value for each pixel in each single-channel RAW image; According to the width and height dimensions of the preset calibration table, each of the single-channel RAW images is divided into at least one first pixel region; Based on the third gain value corresponding to each pixel in the first pixel region, the average gain value of the first pixel region is calculated, and the average gain value is written into the preset correction table at the position corresponding to the first pixel region to obtain the preset correction table corresponding to the preset image color temperature.

8. The vignetting correction method for a panoramic camera according to claim 7, characterized in that, The calculation of the third gain value for each pixel in each single-channel RAW image includes: Extract a second pixel region of a preset pixel size from the central region of each single-channel RAW image; The average brightness of each single-channel RAW image is calculated based on the pixel brightness of each pixel in the second pixel region and the total number of pixels in the second pixel region. For each single-channel RAW image, the third gain value of each pixel in the single-channel RAW image is obtained by dividing the average brightness by the pixel brightness of each pixel in the single-channel RAW image.

9. The vignetting correction method for a panoramic camera according to claim 7, characterized in that, The preset calibration table includes a first calibration table corresponding to the first image color temperature and a second calibration table corresponding to the second image color temperature, wherein the first image color temperature is lower than the second image color temperature; The step of determining a target correction table corresponding to the current image color temperature based on the current image color temperature of the image to be processed includes: When the current image color temperature is lower than or equal to the first image color temperature, the first correction table is determined to be the target correction table; When the current image color temperature is higher than or equal to the second image color temperature, the second correction table is determined as the target correction table.

10. The vignetting correction method for a panoramic camera according to claim 9, characterized in that, The preset calibration table also includes a third calibration table corresponding to the third image color temperature, wherein the third image color temperature is between the first image color temperature and the second image color temperature; The step of determining a target correction table corresponding to the current image color temperature based on the current image color temperature of the image to be processed further includes: When the current image color temperature is higher than the first image color temperature and lower than the second image color temperature, then find the two image color temperatures that are closest to the current image color temperature among the first image color temperature, the second image color temperature and the third image color temperature; The target correction table is generated by interpolating the preset correction table corresponding to the two image color temperatures closest to the current image color temperature.

Images