CN117939307B - Self-adaptive brightness adjusting method suitable for fusion camera - Google Patents
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Abstract
The invention provides a self-adaptive brightness adjusting method suitable for a fusion camera. First, image data, such as infrared imaging, visible light imaging, and the like, is acquired by fusing a plurality of imaging sources of a camera. Then, preprocessing operations such as noise elimination and brightness balance are performed, and brightness information of each image is extracted, wherein the brightness information comprises brightness values of single pixels or average brightness of the whole image. Next, adaptive brightness adjustment is performed according to the calculation result of the brightness information, which may involve adjusting the brightness level of the entire image or adjusting only the brightness of a specific area. And finally, generating a fusion image with the brightness self-adaptive adjusted by fusion processing of the multi-source image with the brightness adjusted. Through the steps, the visual effect of the fusion image can be effectively improved, the dynamic range of the image is enhanced, and meanwhile, the image quality is improved.
Description
Technical Field
The invention belongs to the field of image processing, and particularly relates to a self-adaptive brightness adjusting method suitable for a fusion camera.
Background
In the field of image processing, well known brightness adjustment is typically based on a single source image, such as a cell phone camera or other single camera device. Such conventional brightness adjustment is often only capable of adjusting the brightness level of the entire image and is not capable of accommodating brightness variations in different areas of the image. For scenes with a large dynamic range or special lighting conditions, this approach often fails to meet the requirements.
On the other hand, existing image fusion techniques typically simply superimpose or mix together images from different sources without taking into account luminance information in the images. This may result in non-uniform brightness of the fused image or lack of dynamic range. Furthermore, different image sources may be affected by different light conditions, which may create visual discontinuities or blurs if fused directly.
Disadvantages of the prior art:
The single brightness adjustment strategy cannot meet the requirements under complex illumination conditions, and cannot process scenes with large dynamic ranges. When the fusion technology processes the multi-source image, the influence of brightness information and illumination conditions is not considered, and the brightness of a fusion result may be uneven or the visual effect may be poor.
Processing and fusing of multi-source images typically requires significant computational resources and time, which may be unacceptable in real-time or near real-time applications. Many existing image processing methods lack flexibility and are not easily adjusted or expanded to cope with different application scenarios and requirements.
Disclosure of Invention
The main technical problem to be solved by the invention is how to effectively perform self-adaptive brightness adjustment in the multi-source image fusion process so as to improve the visual effect and dynamic range of the fusion image and simultaneously cope with dynamic change and complex illumination conditions. The invention adjusts the brightness of each source image in a self-adaptive way and flexibly fuses the multi-source images, thereby effectively solving the problems of uneven brightness, small dynamic range and incapability of adapting to complex illumination conditions in the prior art.
In order to achieve the above purpose, the present invention is realized by adopting the following technical scheme: the self-adaptive brightness adjusting method suitable for the fusion camera is characterized by comprising the following steps of: the self-adaptive brightness adjusting method comprises the following steps:
S1, acquiring multi-source image data, namely acquiring the image data by utilizing a plurality of imaging sources of a fusion camera;
s2, preprocessing the image, namely performing necessary preprocessing on the acquired image data, eliminating noise and balancing brightness;
s3, calculating brightness information, namely extracting the brightness information in each image;
s4, self-adaptive adjustment, namely performing self-adaptive brightness adjustment according to the calculated brightness information;
s5, fusion processing, namely carrying out fusion processing on the multi-source image with the brightness adjusted, and generating a fusion image with the brightness adaptively adjusted.
Further, the luminance equalization in S2 is specifically as follows:
S201 calculates a luminance histogram of the image: determining the frequency distribution of the brightness of each pixel, and calculating a brightness histogram of the image;
s202 calculates a cumulative histogram: calculating a cumulative histogram of the image by adding the frequency numbers at all previous levels at each brightness level;
The formula is:
；
Wherein, Is the cumulative distribution function CDF,/>, of gray level i in the imageIs the probability of gray level i in the original image,/>Is the cumulative sum of j=0 to i;
s203 applies histogram equalization: adjusting the brightness of each pixel according to the cumulative histogram; the new luminance value of each pixel is its corresponding value in the cumulative histogram; so that the image with uneven brightness distribution becomes uniform in brightness;
The formula is:
；
Wherein, Is the new brightness value after adjustment, L is the total gray level, which is 256,/>Is the cumulative distribution function CDF of the i-th stage.
Further, the step S3 of calculating the brightness information comprises calculating the brightness of each pixel and calculating the average brightness of the whole image;
Converting the RGB color space into a YUV color space or an HSV color space, wherein Y represents brightness information in the YUV color space and V represents brightness information in the HSV color space;
In the conversion from RGB to YUV, the calculation formula of the luminance Y is:
；
In the above formula R, G, B represents the pixel values of the three channels of red, green and blue of the image respectively;
In the HSV color space, luminance information is represented by a V channel, which is the maximum value among three channels of RGB, namely:
；
luminance information of an image is extracted, each pixel of the image is traversed, and then a luminance value is calculated using the above formula.
Further, the S3 calculation brightness information adopts calculation of the average brightness of the whole image;
The method comprises the following steps:
accumulating the brightness values of all pixels and dividing the brightness values by the total number of the pixels; the formula is:
；
Wherein, For the average brightness of the whole image,/>For the luminance value of each pixel of the original pixels, N is the total number of pixels.
Further, the S4 adaptive adjustment is specifically as follows:
Setting a target brightness level And adjusting the intensity coefficient alpha. Then, a new luminance value/>, for each pixel is calculatedThe formula is as follows:
；
Wherein, Is a new luminance value,/>For the luminance value of each pixel of the original pixel,/>Is the average brightness of the whole image, and alpha is the adjustment intensity coefficient;
in the course of this formula (ii) the formula, The part of (a) is to convert the brightness value of the pixel from average brightness to a range with 0 as the center, and then multiply the brightness value by the adjustment intensity coefficient alpha to increase or decrease the contrast of the image; finally adding the target brightness levelAnd obtaining a new brightness value.
Further, the alpha value range is between 0 and 1.
Further, the S5 fusion process is specifically as follows:
s501, brightness adjustment, namely performing self-adaptive brightness adjustment processing of the steps S1-S4 on each source image;
s502, weight distribution, namely distributing weight to each source image with adjusted brightness; the weight of the image is determined by a number of factors, including the quality, importance, and desired ratio in the fusion result of the source image;
let the weight of each source image be Where i is the sequence number of the source image and the sum of all weights is 1, i.e./>；
；
Wherein,Quality score representing the ith source image,/>Importance score representing the ith source image,/>Representing the sum of the quality scores of all source images;
s503 fusion processing, calculating new brightness of each pixel in the fusion image by using brightness and weight of the source image;
Let the brightness of the source image at the pixel point be Then the new luminance/>, of that pixel in the fused imageCalculated from the following formula:
；
the above formula shows that the new luminance is the sum of the product of the luminance of each source image at that pixel point and its weight;
s504, updating the pixel value of the fusion image at the corresponding pixel point by using the calculated new brightness; in the updating process, consistency and accuracy of color are maintained.
The invention has the beneficial effects that:
1. the self-adaptive brightness adjustment can lead the brightness of the image acquired by the fusion camera to be more balanced, effectively avoid the problems of detail loss and overexposure of the dark part of the image caused by light change, improve the dynamic range of the image and lead the image to have better ornamental value and practicability;
2. the individual parts of the self-adaptive brightness adjusting method can be used as an independent image processing technology, so that the visual quality of the whole image can be improved, and pre-processing can be provided for advanced applications such as image fusion;
3. The brightness adjusting method is suitable for image data obtained by a plurality of imaging sources, is particularly suitable for a fusion camera, and can improve the fusion effect of multi-source images obtained from different angles and under different light conditions, so that the final fusion image is better in visual effect.
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FIG. 1 is a general flow chart of the present invention;
FIG. 2 is a flow chart of luminance balancing according to the present invention;
FIG. 3 is a flow chart of the fusion process of the present invention.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Exemplary embodiments of the present invention are illustrated in the accompanying drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
As shown in fig. 1, the adaptive brightness adjustment method includes:
S1, acquiring multi-source image data, namely acquiring the image data by utilizing a plurality of imaging sources of a fusion camera;
acquiring image data under multi-angle and multi-illumination conditions: the multi-source image data acquired by the fusion camera can contain diversified information from different angles, different field depths, different resolutions, different illumination conditions, and the like, and richer source data is provided for image processing and analysis.
Providing a data source for the following processes such as adaptive brightness adjustment, image fusion and the like: the stored multi-source image data can be used as input in the subsequent steps, and the brightness adjustment and the fusion processing are carried out on the multi-source image data through the steps S2-S5, so that the fusion image with self-adaptive brightness (contrast) is finally output.
S2, preprocessing the image, namely performing necessary preprocessing on the acquired image data, eliminating noise and balancing brightness;
the noise elimination adopts median filtering, and the specific implementation steps are as follows:
A filter window is defined, the size of which is typically an odd size such as 3x3,5x5,7x 7.
And placing a filter window at a position to be processed of the image, wherein pixel values in an area covered by the window form a series.
The sequence is ordered and the number of bits is taken as the pixel value after the pixel position filtering.
And moving the filter window to each pixel position of the image one by one, and repeating the steps until all pixels of the image are processed.
The median filtering has the advantage that edge preserving smoothing can be performed, i.e. the sharpness of the edges of the image can be preserved while eliminating the noise of the image.
As shown in fig. 2, the luminance equalization is specifically as follows:
S201 calculates a luminance histogram of the image: first, in order to determine the frequency distribution of the luminance of each pixel, it is necessary to calculate the luminance histogram of the image. The histogram shows how many pixels in an image have a particular luminance value.
The frequency distribution of the luminance of each pixel is determined, and the luminance histogram of the image is a representation of the image luminance statistics, indicating the number of pixels at various luminance levels in the image. The method is helpful for analyzing the distribution condition of the brightness of the image and identifying the basic attributes of the image, such as contrast, bright and dark areas and the like. An image brightness histogram is generated, and the brightness distribution characteristics of the image are intuitively displayed.
S202 calculates a cumulative histogram: calculating a cumulative histogram of the image by adding the frequency numbers at all previous levels at each brightness level;
The formula is:
（1）；
Wherein, Is the cumulative distribution function CDF,/>, of gray level i in the imageIs the probability of gray level i in the original image,/>Is the cumulative sum of j=0 to i.
A cumulative histogram of the image is calculated which aids in understanding the luminance distribution characteristics in the image by adding the frequency numbers at all previous levels to each luminance level. The slope change of the cumulative histogram may reflect the rate of change of each brightness level in the image, thereby facilitating further processing of the image.
S203 applies histogram equalization: adjusting the brightness of each pixel according to the cumulative histogram; the new luminance value of each pixel is its corresponding value in the cumulative histogram; so that the image with uneven brightness distribution becomes uniform in brightness;
The formula is:
（2）；
Wherein, Is the new brightness value after adjustment, L is the total gray level, which is 256,/>Is the cumulative distribution function CDF of the i-th stage.
The brightness of each pixel is adjusted according to the cumulative histogram. Histogram equalization is an image enhancement technique that aims to flatten the histogram of an image so that the brightness levels in the image are maximally spread out, thereby enhancing the contrast of the image. The image subjected to histogram equalization has more average brightness as a whole, the contrast of the image is obviously improved, the subsequent operation is more convenient, and the visual effect of the image is improved.
S3, calculating brightness information, namely extracting the brightness information in each image; the method aims at extracting brightness information from the image and provides data basis for subsequent brightness adjustment.
The S3 brightness information is calculated by calculating the brightness of each pixel; the method comprises the following steps:
Converting the RGB color space into a YUV color space or an HSV color space, wherein Y represents brightness information in the YUV color space and V represents brightness information in the HSV color space;
In the conversion from RGB to YUV, the calculation formula of the luminance Y is:
（3）；
In the above formula R, G, B represents the pixel values of the three channels of red, green and blue of the image respectively;
In the HSV color space, luminance information is represented by a V channel, which is the maximum of three channels of RGB, namely:
（4）；
luminance information of an image is extracted, each pixel of the image is traversed, and then a luminance value is calculated using the above formula.
Further, the S3 calculation brightness information adopts calculation of the average brightness of the whole image;
The method comprises the following steps:
accumulating the brightness values of all pixels and dividing the brightness values by the total number of the pixels; the formula is:
（5）；
Wherein, For the average brightness of the whole image,/>For the luminance value of each pixel of the original pixels, N is the total number of pixels.
By calculating the brightness information, the brightness distribution condition of the image is known, and then targeted brightness adjustment is performed, so that the adjusted image has a better visual effect.
S4, self-adaptive adjustment, namely performing self-adaptive brightness adjustment according to the calculated brightness information; and S4, a self-adaptive adjustment link is used for carrying out targeted brightness adjustment based on the brightness information and average brightness of each source image obtained and analyzed in the steps S1-S3.
The self-adaptive adjustment of the S4 is specifically as follows:
Setting a target brightness level And adjusting the intensity coefficient alpha. Then, a new luminance value/>, for each pixel is calculatedThe formula is as follows:
（6）；
Wherein, Is a new luminance value,/>For the luminance value of each pixel of the original pixel,/>Is the average brightness of the whole image, and alpha is the adjustment intensity coefficient; the alpha value range is between 0 and 1.
In the course of this formula (ii) the formula,The part of (a) is to convert the brightness value of the pixel from average brightness to a range with 0 as the center, and then multiply the brightness value by the adjustment intensity coefficient alpha to increase or decrease the contrast of the image; finally adding the target brightness levelAnd obtaining a new brightness value.
Adjusting the brightness of an image: and carrying out self-adaptive adjustment on the brightness of the original image according to the calculated image average brightness and the set target brightness. The principle of the adjustment is as follows: by enlarging the pixel luminance difference value around the original average luminance, the contrast of the image is enhanced while making the luminance distribution more approximate to the target luminance. Improving the visual experience: the brightness-adjusted image can provide a better visual perception. The good brightness can make the details of the image clearer, so that the details of the dark part and the bright part are displayed simultaneously, and the integral image observation effect is improved.
After brightness adjustment, the contrast of the image is significantly improved. An image which is originally insufficient in detail in a dark area and a bright area can simultaneously maintain the details of the bright portion and the dark portion after brightness adjustment. Through the adjustment of S4, the situation that the image is locally too bright or too dark caused by factors such as illumination conditions can be eliminated, so that the brightness distribution of the image is more balanced and is closer to the visual perception of human eyes. The method can adapt to various scenes and illumination conditions: s4 is used as a part of self-adaptive brightness adjustment, so that effective brightness adjustment can be carried out on images acquired under various different illumination conditions, and the adaptability and application range of image processing are greatly improved.
S5, fusion processing, namely carrying out fusion processing on the multi-source image with the brightness adjusted, and generating a fusion image with the brightness adaptively adjusted. The process is to fuse the source images after the self-adaptive brightness adjustment to generate a fused image with better visual effect and observation value.
As shown in fig. 3, the S5 fusion process is specifically as follows:
S501, brightness adjustment, namely performing self-adaptive brightness adjustment processing of the steps S1-S4 on each source image; this stage performs the previous steps S1 to S4 for each source image, and performs an adaptive brightness adjustment process. The method aims at adaptively adjusting the brightness of each source image, eliminating brightness variation caused by factors such as illumination, angles and the like, and enabling the brightness of the fused image to be more balanced. The brightness of each source image is adjusted to a relatively uniform range, providing for high quality image fusion.
S502, weight distribution, namely distributing a weight to each source image with adjusted brightness; the weight of the image is determined by a number of factors, including the quality, importance, and desired ratio in the fusion result of the source image; at this stage, each adjusted source image will be assigned a weight that may be determined by factors such as the quality, importance, and desired proportions in the fusion result of the source image. The weight distribution can enable the fused image to better reflect the characteristics of the source image, and the information quantity of the fused image is increased.
Let the weight of each source image beWhere i is the sequence number of the source image and the sum of all weights is 1, i.e./>；
（7）；
Wherein,Quality score representing the ith source image,/>Importance score representing the ith source image,/>Representing the sum of the quality scores of all source images.
Performing quality evaluation on each source image to obtain quality scores. The scoring may be based on image quality factors such as sharpness, noise, etc.
Performing importance evaluation on each source image to obtain importance scores thereof. The scoring may be based on its desired proportion in the final fused image or some specific application scenario requirements.
Scoring the quality of each source imageAnd importance score/>Substituting the weight into the formula (7) to calculate and obtain the weight/>。
S503 fusion processing, calculating new brightness of each pixel in the fusion image by using brightness and weight of the source image; at this stage, a new luminance is calculated for each pixel in the fused image using the luminance of the source image and its weight. The contribution of each source image can be embodied in the fusion process, and the generated fusion image has a better visual effect.
Let the brightness of the source image at a certain pixel point beThen the new luminance/>, of that pixel in the fused imageCalculated from the following formula:
（8）；
the above formula shows that the new luminance is the sum of the product of the luminance of each source image at that pixel point and its weight;
S504, updating the pixel value of the fusion image at the corresponding pixel point by using the calculated new brightness; in the updating process, consistency and accuracy of color are maintained. And updating the pixel value of the fusion image at the corresponding pixel point by using the calculated new brightness. The calculated new luminance is applied to the fused image such that the pixel value of the fused image matches the actual luminance. A final fused image is formed that has balanced brightness and reflects the characteristics of all source images.
In the process of fusing images, color consistency and accuracy are maintained, and it is mainly ensured that the original color information is not changed while the pixel values are adjusted to change the brightness. By operating in color space, in YUV or HSV space, luminance channels are processed separately without affecting the color channels. Or in the RGB color space, the values of the three channels are adjusted in equal proportion, thereby changing the brightness but not the color.
The invention effectively solves the problem of poor image quality caused by the change of ambient light in the photographing or shooting process. The method can realize self-adaptive brightness adjustment, perform pretreatment, brightness information calculation, self-adaptive adjustment and fusion treatment on the image, and generate a fusion image with brightness self-adaptive adjustment, so that the image quality is obviously improved.
Self-adaptive adjustment: after the brightness of each pixel is calculated, the self-adaptive adjustment of the brightness is realized according to the set target brightness level and the adjustment intensity coefficient alpha, so that the quality and the ornamental value of the image are improved.
Brightness adjustment: the histogram equalization method is adopted to adjust the brightness of the image, so that the brightness distribution of the image is more uniform, the contrast and detail expression of the image are enhanced, and the quality of the image is improved.
Source image weight assignment: and each source image is distributed with a weight, so that each source image can adaptively contribute to own brightness information according to factors such as importance, quality and the like in the image fusion process, and a better fusion image is generated.
Fusion treatment: and calculating the new brightness of each pixel in the fusion image through the brightness and the weight of the source image, and updating the pixel value of the fusion image at the corresponding pixel point, so as to ensure the consistency and the accuracy of the color and obtain the fusion image with better quality.
These three steps (S2 preprocessing, S3 calculation of luminance information, S4 adaptation) do improve some specific problems of the image, such as noise, luminance imbalance, contrast, etc., when they are performed separately. The effects of these three steps not only introduce individual improvements, but also promote and strengthen each other so that the overall effect is greater than a simple superposition of the effects of the individual steps.
First, the S2 preprocessing step provides a clearer, more uniform input image for subsequent steps. This is crucial for subsequent luminance information extraction and adaptive luminance adjustment. The accuracy of the brightness information extraction can be ensured by noise elimination, and the brightness of the adjusted image is more uniform by brightness equalization, so that the overall quality of the image is improved.
The process of calculating the luminance information in S3 then not only allows knowledge of the overall luminance distribution of the image, which is crucial for the adaptive adjustment in S4 step, but also helps to optimize the effect of the S2 preprocessing step to a certain extent, since the luminance information calculation requires a noise-free and luminance-uniform image.
Finally, after the self-adaptive brightness adjustment of S4 is executed, not only is the image with more uniform brightness obtained, but also different parts of the image are ensured to be properly adjusted according to the brightness information of the image, so that the optimization of the whole effect is achieved. The operation of the step further improves the color balance and the contrast of the image and improves the visual effect of the image.
Meanwhile, the effect of the step S4 also depends on the accurate brightness information provided by the step S3, and the accurate extraction of the brightness information requires the pretreatment of the step S2.
Thus, there is a close relationship and interdependence between these three steps, whose combined effect is overall greater than a simple superposition of the effects of the individual steps.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
It should be understood that the detailed description of the technical solution of the present invention, given by way of preferred embodiments, is illustrative and not restrictive. Modifications of the technical solutions described in the embodiments or equivalent substitutions of some technical features thereof may be performed by those skilled in the art on the basis of the present description; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. The self-adaptive brightness adjusting method suitable for the fusion camera is characterized by comprising the following steps of: the self-adaptive brightness adjusting method comprises the following steps:
S1, acquiring multi-source image data, namely acquiring the image data by utilizing a plurality of imaging sources of a fusion camera;
s2, preprocessing the image data, namely preprocessing the acquired image data, eliminating noise and balancing brightness;
s3, calculating brightness information, namely extracting the brightness information in each image;
s4, self-adaptive adjustment, namely performing self-adaptive brightness adjustment according to the calculated brightness information;
The self-adaptive adjustment of the S4 is specifically as follows:
Setting a target brightness level And adjusting the intensity coefficient α; then, a new luminance value/>, for each pixel is calculatedThe formula is as follows:
；
Wherein, Is a new luminance value,/>For the luminance value of each pixel of the original pixel,/>Is the average brightness of the whole image, and alpha is the adjustment intensity coefficient;
converting the brightness value of the pixel from average brightness to a range taking 0 as a center, and multiplying the brightness value by an adjustment intensity coefficient alpha to increase or decrease the contrast of the image; finally, adding the target brightness level/> Obtaining a new brightness value;
adjusting the brightness of the image, and carrying out self-adaptive adjustment on the brightness of the original image according to the calculated average brightness of the image and the set target brightness;
s5, fusion processing, namely carrying out fusion processing on the multi-source image with the brightness adjusted, and generating a fusion image with the brightness adaptively adjusted.
2. An adaptive brightness adjustment method for a fusion camera according to claim 1, characterized in that: the brightness equalization in S2 is specifically as follows:
S201 calculates a luminance histogram of the image: determining the frequency distribution of the brightness of each pixel, and calculating a brightness histogram of the image;
s202 calculates a cumulative histogram: calculating a cumulative histogram of the image by adding the frequency numbers at all previous levels at each brightness level;
The formula is:
；
Wherein, Is the cumulative distribution function CDF,/>, of gray level i in the imageIs the probability of gray level i in the original image,/>Is the cumulative sum of j=0 to i;
s203 applies histogram equalization: adjusting the brightness of each pixel according to the cumulative histogram; the new luminance value of each pixel is its corresponding value in the cumulative histogram; so that the image with uneven brightness distribution becomes uniform in brightness;
The formula is:
；
Wherein, Is the new brightness value after adjustment, L is the total gray level, which is 256,/>Is the cumulative distribution function CDF of the i-th stage.
3. An adaptive brightness adjustment method for a fusion camera according to claim 1, characterized in that: s3, calculating brightness information, including calculating brightness of each pixel and calculating average brightness of the whole image;
The brightness of each pixel is calculated as follows:
Converting the RGB color space into a YUV or HSV color space, wherein Y represents luminance information in the YUV color space and V represents luminance information in the HSV color space;
In the conversion from RGB to YUV, the calculation formula of the luminance Y is:
；
In the above formula R, G, B represents the pixel values of the three channels of red, green and blue of the image respectively;
In the HSV color space, luminance information is represented by a V channel, which is the maximum value among three channels of RGB, namely:
；
Luminance information of the image is extracted, each pixel of the image is traversed, and then a luminance value is calculated using the above formula.
4. A method of adaptive brightness adjustment for a fusion camera according to claim 3, characterized in that: the average brightness of the whole image is calculated as follows:
accumulating the brightness values of all pixels and dividing the brightness values by the total number of the pixels; the formula is:
；
Where N is the total number of pixels.
5. An adaptive brightness adjustment method for a fusion camera according to claim 1, characterized in that: the alpha value range is between 0 and 1.
6. An adaptive brightness adjustment method for a fusion camera according to claim 1, characterized in that: the S5 fusion process is specifically as follows:
s501, brightness adjustment, namely performing self-adaptive brightness adjustment processing of the steps S1-S4 on each source image;
s502, weight distribution, namely distributing weight to each source image with adjusted brightness; the weight of the image is determined by a number of factors, including the quality, importance, and desired ratio in the fusion result of the source image;
let the weight of each source image be Where i is the sequence number of the source image and the sum of all weights is 1, i.e./>；
；
Wherein,Quality score representing the ith source image,/>Importance score representing the ith source image,/>Representing the sum of the quality scores of all source images;
s503 fusion processing, calculating new brightness of each pixel in the fusion image by using brightness and weight of the source image;
Let the brightness of the source image at the pixel point be Then the new luminance/>, of that pixel in the fused imageCalculated from the following formula:
；
the above formula shows that the new luminance is the sum of the product of the luminance of each source image at that pixel point and its weight;
s504, updating the pixel value of the fusion image at the corresponding pixel point by using the calculated new brightness; in the updating process, consistency and accuracy of color are maintained.
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