A digestive endoscopy image enhancement and analysis system

Abstract

This invention discloses a digestive endoscopy image enhancement and analysis system, belonging to the field of image enhancement technology. This invention utilizes the cooperation of an image acquisition module and a transmission module to capture the original brightness-related data of digestive endoscopy images. It then uses the cooperation of a storage module and a transmission module to extract a real-time updated system enhancement benchmark. Based on this benchmark, the original digestive endoscopy images are enhanced. An enhancement adjustment module sequentially calculates and outputs an enhancement factor BZ, an equalization adjustment coefficient J, and a dynamic range compression value DF. Based on the dynamic range compression value DF, the enhancement adjustment module further enhances the digestive endoscopy images. This invention achieves fine adjustment of image contrast, optimization of brightness uniformity, and intelligent adjustment of dynamic range. These beneficial effects collectively improve the overall image quality and provide the system with a more reliable and efficient image enhancement processing tool.

Description

Technical Field

[0001] This invention relates to the field of image enhancement technology, and more particularly to a digestive endoscopy image enhancement and analysis system. Background Technology

[0002] Digestive endoscopy refers to equipment that directly acquires images through the digestive tract, including those transmitted via ultrasound and X-rays. Based on the type of endoscope used, it can be classified as esophagoscopy, gastroscopy, and duodenoscopy. Endoscopic examination is the core technology and gold standard for the early diagnosis and screening of esophageal and gastrointestinal cancers. Performing endoscopic screening for esophageal and gastrointestinal cancers can detect and treat precancerous lesions, significantly reducing the incidence and mortality of malignant tumors of the digestive tract. In recent years, with breakthroughs in artificial intelligence technology, image recognition technology has made progress in medical imaging and endoscopy. Computer-aided diagnostic systems are expected to act as a "third eye" for endoscopists, providing real-time assistance and supervision.

[0003] However, existing methods lack fine control over contrast, which can lead to lesion details being obscured in some cases due to insufficient contrast. Furthermore, the uniformity of image brightness is often poor due to various factors such as lighting conditions and lens contamination during the acquisition of gastrointestinal images, which can affect the accurate judgment of lesions. In addition, existing methods for adjusting the dynamic range of image enhancement often use fixed parameters and simple linear transformations, which cannot be flexibly adjusted according to the actual situation of the image. This results in overexposure and underexposure in some areas of the enhanced image. Summary of the Invention

[0004] The technical problem to be solved by the present invention is that the existing technology has the disadvantages of insufficient contrast enhancement, poor brightness uniformity and inflexible dynamic range adjustment. To this end, we propose a digestive endoscopy image enhancement analysis system.

[0005] The technical solution mainly includes: a digestive endoscopy image enhancement analysis system, specifically comprising the following steps:

[0006] Step 1: Using the cooperation of the image acquisition module and the transmission module, capture the original brightness-related data of the gastrointestinal endoscopy image and transmit it to the enhancement adjustment module;

[0007] Step 2: Using the cooperation of the storage module and the transmission module, extract the real-time updated system enhancement reference, and perform image enhancement on the original digestive endoscopy image based on the system enhancement reference, and transmit the relevant enhanced brightness-related data to the enhancement adjustment module.

[0008] Step 3: Using the enhancement adjustment module, calculate and output the enhancement factor BZ, the equalization adjustment coefficient J, and the dynamic range compression range value DF in sequence.

[0009] Step 4: Based on the dynamic range compression range value DF, the endoscopy images are enhanced using the enhancement adjustment module;

[0010] Step 5: After completing the enhancement processing of the digestive endoscopy images, output them using the output module;

[0011] The enhancement adjustment module includes an enhanced contrast difference unit, an adaptive histogram equalization unit, and a dynamic range compression adjustment unit.

[0012] Preferably, the calculation formula for the enhanced contrast difference unit is as follows:

[0013] ;

[0014] in:

[0015] BZ is the enhancing factor;

[0016] BC orig BC represents the standard deviation of the original image brightness. orig The standard deviation reflecting the brightness of unenhanced gastrointestinal endoscopic images;

[0017] BC enh BC represents the standard deviation of the image brightness after target enhancement. enh The standard deviation of the image brightness after intelligent enhancement of digestive endoscopy images based on the system enhancement benchmark;

[0018] The square root operation is used to make the result of BZ a positive number, and when B and C are in a positive state... enh >BC orig When BZ is greater than 1, it indicates that contrast needs to be enhanced; when BC... enh <BC orig When BZ is less than 1;

[0019] Original image brightness standard deviation BC orig Standard deviation of target-enhanced image brightness BC enh The calculation formulas are as follows:

[0020] ;

[0021] ;

[0022] N represents the original pixel number, L i,orig Let L be the brightness value of the i-th original image. orig,avg This represents the average brightness of the original image.

[0023] M represents the enhanced pixel, L i,enh Let L be the brightness value of the enhanced image of the i-th target. enh,avgThe enhanced image brightness value for the target.

[0024] Preferably, the system enhancement benchmark is a real-time updated average enhancement level obtained by summarizing the enhanced brightness of the population after intelligent enhancement of digestive endoscopy images under the big data stored in the storage module and calculating the average value. The specific calculation formula of the system enhancement benchmark is as follows:

[0025] ;

[0026] JSD is the system enhancement baseline; N is the total number of users who have completed enhancements. i This is the i-th end enhancement brightness value;

[0027] Furthermore, the brightness of the original image is enhanced based on the brightness value obtained from the real-time updated system enhancement benchmark JSD.

[0028] Preferably, the calculation formula for the adaptive histogram equalization unit is as follows:

[0029] ;

[0030] in:

[0031] J is the equalization adjustment coefficient, which reflects the effect of adjusting the equalization based on the introduced image enhancement requirements.

[0032] L orig,avg This represents the average brightness of the original image.

[0033] L min This represents the minimum brightness value of the original image.

[0034] L max This represents the maximum brightness of the original image.

[0035] It is a brightness-related adjustment factor, which is based on and The relationship is used to adjust the equalization weights, when the average brightness of the original image L orig,avg Approximately the maximum brightness of the original image L max When the adjustment factor increases, the average brightness L of the original image... orig,avg Approximately the minimum brightness value L of the original image min When this happens, the adjustment factor decreases;

[0036] The multiplication of the equalization adjustment coefficient J with the adjustment factor achieves a comprehensive adjustment of contrast and brightness.

[0037] Preferably, the calculation formula for the compression adjustment dynamic range unit is as follows:

[0038] ;

[0039] in:

[0040] DF represents the dynamic range compressed range value;

[0041] DF enh To enhance the brightness range of the post-image;

[0042] DF min This represents the minimum value within the image brightness range.

[0043] DF max This represents the maximum value within the image brightness range.

[0044] The square root is taken to account for the effect of the equalization adjustment coefficient J when calculating DF, while maintaining the result without affecting the division by DF. Calculated proportional relationships;

[0045] This controls the adjustment range of the dynamic range.

[0046] Preferably, the dynamic range compression range value DF and the enhanced image brightness range value DF are used as the basis. enh Furthermore, the dynamic range compression range value DF is not equal to the enhanced image brightness range value DF. enh In this case, the enhancement adjustment module is used for intelligent adjustment, and the endoscopy images are enhanced according to the result value of the dynamic range compression range value DF. The specific processing includes:

[0047] If the dynamic range compression range value DF is lower than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Intelligent enlargement;

[0048] If the dynamic range compression range value DF is higher than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Perform intelligent scaling down;

[0049] Iteration continues until the dynamic range compression range value DF equals the enhanced image brightness range value DF. enh The situation.

[0050] Preferably, the equipment used in the image acquisition module includes a digestive endoscopy device, the equipment used in the transmission module includes a data transmission device, the equipment used in the storage module includes a storage device, the equipment used in the enhancement and adjustment module includes an image enhancement and analysis system, and the equipment used in the output module includes a display device.

[0051] The technical effects and advantages of this invention are as follows:

[0052] In this invention, an enhanced contrast difference unit driven by an artificial intelligence algorithm enables the system to determine the brightness standard deviation BC of the original image. orig Standard deviation of target-enhanced image brightness BC enh Furthermore, with the powerful computing capabilities of artificial intelligence, it ensures accurate identification of contrast differences in complex image data, which helps to highlight lesion details and improve diagnostic accuracy.

[0053] In this invention, the adaptive histogram equalization unit combines an enhancement factor BZ and a brightness-related adjustment factor. Through an artificial intelligence adaptive adjustment mechanism, it achieves brightness uniformity. The artificial intelligence algorithm can automatically adjust the weight of histogram equalization according to the real-time characteristics of the image, thereby effectively reducing the impact of lighting conditions and lens contamination on image quality.

[0054] In this invention, the dynamic range compression adjustment unit utilizes artificial intelligence technology and intelligently generates an optimized image brightness range based on the equalization adjustment coefficient J and the enhanced image brightness range. This ensures that the enhanced image has sufficient contrast without being overexposed or underexposed. At the same time, the AI-driven iterative adjustment mechanism enables the system to continuously approach the optimal enhancement effect, thereby improving the overall image quality.

[0055] In addition, the entire image enhancement process is completed automatically by the system without the need for manual parameter adjustment. This high-efficiency computing and decision-making capabilities of artificial intelligence not only improve processing efficiency but also ensure the consistency of enhancement results and avoid errors caused by human factors. Attached Figure Description

[0056] Figure 1 This is a flowchart of the method for this digestive endoscopy image enhancement analysis system;

[0057] Figure 2 This is a schematic diagram of the overall structure of the present invention;

[0058] Figure 3 This is a schematic diagram of the enhanced adjustment module in this invention;

[0059] Figure 4 This is a schematic diagram illustrating the iterative enhancement of the dynamic range compression range value DF in this invention. Detailed Implementation

[0060] The present invention will now be described in further detail with reference to the accompanying drawings and preferred embodiments.

[0061] Reference Figures 1 to 4As shown, the present invention provides a technical solution: a digestive endoscopy image enhancement analysis system, specifically including the following steps:

[0062] Step 1: Using the cooperation of the image acquisition module and the transmission module, capture the original brightness-related data of the gastrointestinal endoscopy image and transmit it to the enhancement adjustment module;

[0063] Step 2: Using the cooperation of the storage module and the transmission module, extract the real-time updated system enhancement reference, and perform image enhancement on the original digestive endoscopy image based on the system enhancement reference, and transmit the relevant enhanced brightness-related data to the enhancement adjustment module.

[0064] Step 3: Using the enhancement adjustment module, calculate and output the enhancement factor BZ, the equalization adjustment coefficient J, and the dynamic range compression range value DF in sequence.

[0065] Step 4: Based on the dynamic range compression range value DF, the endoscopy images are enhanced using the enhancement adjustment module;

[0066] Step 5: After completing the enhancement processing of the digestive endoscopy images, output them using the output module;

[0067] The enhancement adjustment module includes an enhanced contrast difference unit, an adaptive histogram equalization unit, and a compressed adjustment dynamic range unit.

[0068] The equipment used in the image acquisition module includes a digestive endoscopy device; the equipment used in the transmission module includes a data transmission device; the equipment used in the storage module includes a storage device; the equipment used in the enhancement and adjustment module includes an image enhancement and analysis system; and the equipment used in the output module includes a display device.

[0069] In this embodiment, by utilizing the cooperation of the image acquisition module and the transmission module, the artificial intelligence system automatically captures the original brightness-related data of the gastrointestinal endoscopy images and transmits it to the enhancement adjustment module. Artificial intelligence technology ensures accurate data acquisition and efficient transmission, providing a reliable foundation for subsequent processing. The storage module stores a large amount of image data. Through analysis of this big data, the artificial intelligence system extracts a real-time updated system enhancement benchmark. This benchmark is obtained by summarizing and calculating the average enhanced brightness of the population after intelligent enhancement of gastrointestinal endoscopy images. Based on this benchmark, the system enhances the original gastrointestinal endoscopy images and transmits the relevant enhanced brightness-related data to the enhancement adjustment module. Supported by the artificial intelligence algorithm, the enhancement adjustment module sequentially calculates and outputs the enhancement factor BZ, the equalization adjustment coefficient J, and the dynamic range compression range value DF. Thus, the enhanced contrast difference unit, the adaptive histogram equalization unit, and the dynamic range compression adjustment unit each undertake different computational purposes in the gastrointestinal endoscopy image enhancement analysis system. Together, they constitute a complete image enhancement algorithm framework. By accurately calculating these factors, fine adjustment of image contrast, optimization of brightness uniformity, and intelligent adjustment of dynamic range can be achieved, thereby improving the overall quality of the enhanced image.

[0070] Reference Figure 1 and Figure 3 As shown, the calculation formula for the enhanced contrast difference unit is as follows:

[0071] ;

[0072] in:

[0073] BZ is the enhancing factor;

[0074] BC orig BC represents the standard deviation of the original image brightness. orig The standard deviation reflecting the brightness of unenhanced gastrointestinal endoscopic images;

[0075] BC enh BC represents the standard deviation of the image brightness after target enhancement. enh The standard deviation of the image brightness after intelligent enhancement of digestive endoscopy images based on the system enhancement benchmark;

[0076] The system enhancement benchmark is a real-time updated average enhancement level obtained by summarizing the enhanced brightness of images of people who have undergone intelligent enhancement of digestive endoscopy images under the big data stored in the storage module, and calculating the average value. The specific calculation formula for the system enhancement benchmark is as follows:

[0077] ;

[0078] JSD is the system enhancement baseline; N is the total number of users who have completed enhancements. i This is the i-th end enhancement brightness value;

[0079] Furthermore, the brightness of the original image is enhanced based on the brightness value obtained from the real-time updated system enhancement benchmark JSD;

[0080] The square root operation is used to make the result of BZ a positive number, and when B and C are in a positive state... enh >BC orig When BZ is greater than 1, it indicates that contrast needs to be enhanced; when BC... enh <BC orig When BZ is less than 1;

[0081] Original image brightness standard deviation BC orig Standard deviation of target-enhanced image brightness BC enh The calculation formulas are as follows:

[0082] ;

[0083] ;

[0084] N represents the original pixel number, L i,orig Let L be the brightness value of the i-th original image. orig,avg This represents the average brightness of the original image.

[0085] M represents the enhanced pixel, L i,enh Let L be the brightness value of the enhanced image of the i-th target. enh,avg The enhanced image brightness value for the target.

[0086] In the enhanced contrast difference unit of this embodiment, the square root of the symbol is used to calculate the contrast enhancement factor BZ, that is... Among them, the standard deviation of the original image brightness BC orig Standard deviation of target-enhanced image brightness BC enh These were derived through complex artificial intelligence calculations, and This reflects the brightness standard deviation of the original image and the enhanced image. The precise calculations by artificial intelligence ensure that the enhancement factor BZ accurately reflects the image contrast requirements, enabling fine-tuning of image contrast. The square root is added here to ensure that the enhancement factor BZ is a positive number and maintains a reasonable proportional relationship with the standard deviation. The standard deviation itself is a non-negative value, but direct division will yield results greater than or less than 1. The square root operation ensures that the result is always positive, and when BC... enh >BC orig When the enhancement factor BZ is greater than 1, it indicates that contrast enhancement is needed. enh <BCorig At that time, the enhancement factor BZ is less than 1, but in practical applications, BC enh <BC orig The situation will not occur because the goal of the enhanced contrast difference unit is to enhance contrast. In addition, the square root operation makes the change of the enhancement factor BZ and the ratio of the standard deviation change smoother, avoiding the abrupt change caused by direct division.

[0087] In this enhanced contrast difference unit, BC orig It reflects the degree of brightness fluctuation in the original image, while BC enh Based on the real-time updated system enhancement benchmark, the expected brightness fluctuation of the enhanced image is determined. By accurately calculating BZ, it can ensure that the enhanced image maintains the overall brightness distribution while significantly improving local contrast, thereby making the key information of the lesion more prominent and the details more clearly visible in the image.

[0088] Enhanced image contrast helps to accurately identify and analyze lesions. High-contrast images can clearly show the shape, edge and texture features of lesions, thereby improving the accuracy and reliability of lesion diagnosis.

[0089] The enhancement factor BZ is calculated by taking into account the original image brightness standard deviation BC. orig This means that the enhancement factor BZ can be adaptively adjusted according to different original image conditions.

[0090] Reference Figure 1 and Figure 3 As shown in this implementation scheme, the calculation formula for the adaptive histogram equalization unit is as follows:

[0091] ;

[0092] in:

[0093] J is the equalization adjustment coefficient, which reflects the effect of adjusting the equalization based on the introduced image enhancement requirements.

[0094] L orig,avg This represents the average brightness of the original image.

[0095] L min This represents the minimum brightness value of the original image.

[0096] L max This represents the maximum brightness of the original image.

[0097] It is a brightness-related adjustment factor, which is based on and The relationship is used to adjust the equalization weights, when the average brightness of the original image Lorig,avg Approximately the maximum brightness of the original image L max When the adjustment factor increases, the average brightness L of the original image... orig,avg Approximately the minimum brightness value L of the original image min When this happens, the adjustment factor decreases;

[0098] The multiplication of the equalization adjustment coefficient J with the adjustment factor achieves a comprehensive adjustment of contrast and brightness.

[0099] In this embodiment, the adaptive histogram equalization unit incorporates artificial intelligence technology, based on formula BZ and the formula within parentheses. The computational component enables the AI ​​algorithm to adaptively adjust the equalization weights based on the brightness characteristics of the original image, achieving a comprehensive adjustment of contrast and brightness. The multiplication between the calculation components is to combine information from contrast and average brightness to adaptively adjust the histogram equalization effect. The calculation part is a brightness adjustment factor, which is based on and The relationship is used to adjust the equalization weights, when the average brightness of the original image L orig,avg Approximately the maximum brightness of the original image L max When the adjustment factor increases, it indicates that a stronger equalization effect is needed to brighten the shadows. When the average brightness of the original image L... orig,avg Approximately the minimum brightness value L of the original image min When the adjustment factor decreases, it indicates that a weaker equalization effect is needed to avoid overexposure. The multiplication of the enhancement factor BZ and the adjustment factor achieves a comprehensive adjustment of contrast and brightness.

[0100] The equalization adjustment coefficient J effectively improves the brightness uniformity of the image by adjusting the weight of histogram equalization. It combines the information of the enhancement factor BZ to ensure that the enhanced image has a more uniform brightness distribution while the contrast is enhanced. This helps to reduce the interference of brightness unevenness and improve the readability of the image.

[0101] The equalization adjustment coefficient J not only considers the enhancement factor BZ, but also incorporates the average brightness information of the original image, i.e. In terms of computation, this allows the enhanced image to achieve the best balance between contrast and brightness. High-contrast images can highlight key information such as lesions, while uniform brightness distribution helps to more accurately determine the boundaries and morphology of lesions.

[0102] The adaptive adjustment mechanism enables the equalization adjustment coefficient J to maintain a stable enhancement effect under different image conditions. Whether the image is low-brightness or high-brightness, the equalization adjustment coefficient J can optimize the image quality through intelligent adjustment of the system enhancement benchmark, ensuring that the enhanced image has consistency and predictability.

[0103] Reference Figure 1 and Figure 4 As shown in this implementation scheme, the calculation formula for the compression adjustment dynamic range unit is as follows:

[0104] ;

[0105] in:

[0106] DF represents the dynamic range compressed range value;

[0107] DF enh To enhance the brightness range of the post-image;

[0108] DF min This represents the minimum value within the image brightness range.

[0109] DF max This represents the maximum value within the image brightness range.

[0110] The square root is taken to account for the effect of the equalization adjustment coefficient J when calculating DF, while maintaining the result without affecting the division by DF. Calculated proportional relationships;

[0111] This controls the adjustment range of the dynamic range.

[0112] In the compression adjustment dynamic range unit of this embodiment The square root of the numerator is to ensure that the calculation of the dynamic range compression range value DF considers both the effect of the equalization adjustment coefficient J and the principle of dividing by the denominator. The reasonable proportional relationship, the square root operation makes the equalization adjustment coefficient J and As the product increases, the growth rate of the dynamic range compression value DF will not be too fast, thus avoiding excessive compression of the dynamic range. Furthermore, the square root operation makes the calculation of the dynamic range compression value DF smoother and more stable. This is to obtain the offset of the enhanced image brightness range relative to the minimum brightness value. This offset reflects the dynamic range of image brightness variation and is an important parameter for calculating the dynamic range compression value DF. It is obtained by subtracting the minimum image brightness range DF. minIt can ensure that the dynamic range compression range value DF is calculated only for the changes in the brightness range, rather than the entire brightness range itself. It is worth mentioning that its artificial intelligence calculation ensures that the dynamic range adjustment takes into account the influence of the equalization adjustment coefficient J and maintains a reasonable proportional relationship, thus avoiding overexposure and underexposure.

[0113] The dynamic range compression value DF calculated by the dynamic range compression adjustment unit can be adaptively adjusted according to the enhanced image brightness range. It combines the information of the equalization adjustment coefficient J to ensure that the dynamic range of the image is kept within a reasonable range. This helps to avoid overexposure and underexposure, thereby improving the overall image quality.

[0114] By optimizing the dynamic range, the dynamic range compression value DF further improves the overall image quality. This allows the enhanced image to maintain high contrast while also achieving a more reasonable brightness distribution. When the dynamic range compression value DF and the enhanced image brightness range value DF... enh When the values ​​are equal, no iterative adjustments are needed. This means that the system can obtain the best enhancement effect in a single calculation, thereby reducing processing time and computational resource consumption, which helps to improve the system's real-time performance and efficiency.

[0115] Reference Figure 1 and Figure 4 As shown in this implementation scheme: based on the dynamic range compression range value DF and the enhanced image brightness range value DF... enh The enhancement adjustment module, under the control of artificial intelligence, enhances the gastrointestinal endoscopy images, and ensures that the dynamic range compression value DF is not equal to the enhanced image brightness value DF. enh In this case, the artificial intelligence system will perform intelligent adjustments, utilizing the enhancement adjustment module, and specifically enhance the digestive endoscopy images according to the result value of the dynamic range compression range (DF). The specific processing includes:

[0116] If the dynamic range compression range value DF is lower than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Intelligent enlargement;

[0117] If the dynamic range compression range value DF is higher than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Perform intelligent scaling down;

[0118] Iterate until the dynamic range compression range value DF equals the enhanced image brightness range value DF. enhIn this case, after the enhancement processing of the digestive endoscopy images is completed, the output module outputs the enhanced images. The artificial intelligence system ensures that the output images have high contrast, uniform brightness and reasonable dynamic range, and can clearly show the detailed features of the lesions.

[0119] This embodiment uses cyclic adjustments to continuously optimize the enhancement strategy, making the calculation of the enhancement factor BZ more accurate and reasonable. The cyclical influence mechanism ensures a high degree of consistency in enhancement effects under different image conditions. Through continuous iteration and adjustment, the system can gradually adapt to various complex image conditions, improving its overall adaptability. Specifically, this adaptability means that when the dynamic range compression range value DF is lower than the enhanced image brightness range value DF... enh At that time, the dynamic range of the image was relatively small, with some areas being too dark or too bright, resulting in loss of detail. To match the dynamic range of the image with the brightness range of the enhanced image, the system performs enhancement processing. Through the enhancement adjustment module, the system increases the dynamic range of the image, making dark areas brighter and bright areas darker, thereby revealing more detail and making the image clearer. However, the dynamic range compression value DF is higher than the brightness range value DF of the enhanced image. enh In such cases, it means that the dynamic range of the image is too large, which will result in overexposure and underexposure. In order to optimize the image quality, the system will perform attenuation processing, that is, compress the dynamic range of the image to make the brightness distribution of the image more reasonable, avoid overexposure and underexposure, and improve the overall image quality.

[0120] By comprehensively utilizing the enhanced contrast difference unit, adaptive histogram equalization unit, and dynamic range compression adjustment unit, the digestive endoscopy image enhancement analysis system can achieve intelligent and automated image enhancement processing. The system can automatically adjust image parameters according to image characteristics and enhancement requirements to achieve the best enhancement effect. Specifically, the dynamic range compression range value DF in the dynamic range compression adjustment unit plays a crucial role; it can adjust the enhanced image brightness range value DF based on the enhanced image brightness range value. enhThe system intelligently generates an optimized image brightness range using the equalization adjustment coefficient J. This mechanism ensures that the enhanced image has sufficient contrast without overexposure or underexposure, thereby improving the dynamic range performance of the image. The image contrast enhancement factor BZ in the enhanced contrast difference unit is used to calculate the contrast difference between the current image and the image to be enhanced. By adjusting the value of the enhancement factor BZ, the system can intelligently enhance the contrast of the image, making the details in the image clearer. The equalization adjustment coefficient J in the adaptive histogram equalization unit combines contrast and brightness information to adjust the weights in the histogram equalization process. By adjusting the equalization adjustment coefficient J, the system can achieve uniform image brightness and reduce the impact of brightness unevenness on image quality. Thus, the enhanced contrast difference unit, the adaptive histogram equalization unit, and the dynamic range compression adjustment unit together form a closed-loop enhancement adjustment cycle. In this cycle, the dynamic range compression range value DF, the equalization adjustment coefficient J, and the enhancement factor BZ are interconnected and influence each other, forming a dynamic adjustment process. Through continuous iterative optimization, the system can gradually approach the optimal enhancement effect.

[0121] The intelligent processing and adjustment of the digestive endoscopy image enhancement analysis system can significantly improve the quality of images. The enhanced images have higher contrast and brightness uniformity, which makes it easier to observe the details of lesions, thereby improving the accuracy of enhanced images. Furthermore, through automated and intelligent image enhancement processing, the system can reduce the need for human intervention, and the automated and intelligent processing flow can also significantly improve work efficiency.

[0122] In summary, the gastrointestinal endoscopy image enhancement analysis system achieves intelligent and automated image enhancement by comprehensively utilizing the enhanced contrast difference unit, adaptive histogram equalization unit, and dynamic range compression adjustment unit. This system can significantly improve image quality, reduce the need for human intervention, increase work efficiency, and ultimately improve the accuracy of enhanced images.

[0123] It should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should also be within the scope of protection of this invention.

Claims

1. A digestive endoscopy image enhancement analysis system, characterized in that, Specifically, the steps include the following: Step 1: Using the cooperation of the image acquisition module and the transmission module, capture the original brightness-related data of the gastrointestinal endoscopy image and transmit it to the enhancement adjustment module; Step 2: Using the cooperation of the storage module and the transmission module, extract the real-time updated system enhancement benchmark, and perform image enhancement on the original digestive endoscopy images based on the system enhancement benchmark; Step 3: Using the enhancement adjustment module, calculate and output the enhancement factor BZ, the equalization adjustment coefficient J, and the dynamic range compression range value DF in sequence. Step 4: Based on the dynamic range compression range value DF, the endoscopy images are enhanced using the enhancement adjustment module; Step 5: After completing the enhancement processing of the digestive endoscopy images, output them using the output module; The enhancement adjustment module includes an enhanced contrast difference unit, an adaptive histogram equalization unit, and a compressed adjustment dynamic range unit. The calculation formula for the enhanced contrast difference unit is as follows: ； in: BZ is the enhancing factor; BC orig The standard deviation of the original image brightness; BC enh Standard deviation of image brightness after target enhancement; The calculation formula for the adaptive histogram equalization unit is as follows: ； in: J is the equalization adjustment coefficient; L orig,avg This represents the average brightness of the original image. L min This represents the minimum brightness value of the original image. L max This represents the maximum brightness of the original image. The calculation formula for the compression adjustment dynamic range unit is as follows: ； in: DF represents the dynamic range compressed range value; DF enh To enhance the brightness range of the post-image; DF min This represents the minimum value within the image brightness range. DF max This represents the maximum value within the image brightness range.

2. The digestive endoscopy image enhancement analysis system according to claim 1, characterized in that: BC orig The standard deviation reflecting the brightness of unenhanced gastrointestinal endoscopic images; BC enh The standard deviation of the image brightness after intelligent enhancement of digestive endoscopy images based on the system enhancement benchmark; When BC enh >BC orig When BZ is greater than 1, it indicates that contrast needs to be enhanced; when BC... enh <BC orig When BZ is less than 1.

3. The digestive endoscopy image enhancement analysis system according to claim 2, characterized in that: The system enhancement benchmark is a real-time updated average enhancement level obtained by summarizing the enhanced brightness of the population after intelligent enhancement of digestive endoscopy images under the big data stored in the storage module and calculating the average value. The specific calculation formula of the system enhancement benchmark is as follows: ； JSD is the system enhancement baseline; N is the total number of users who have completed enhancements. i The i-th end enhancement brightness value is used to enhance the brightness of the original image based on the brightness value obtained from the real-time updated system enhancement benchmark JSD, and then the relevant enhanced brightness-related data is transmitted to the enhancement adjustment module.

4. The digestive endoscopy image enhancement analysis system according to claim 3, characterized in that: J reflects the adjustment of the equalization effect based on the introduction of image enhancement requirements; It is a brightness-related adjustment factor, which is based on and The relationship is used to adjust the equalization weights, when the average brightness of the original image L orig,avg Approximately the maximum brightness of the original image L max When the adjustment factor increases, the average brightness L of the original image... orig,avg Approximately the minimum brightness value L of the original image min When this happens, the adjustment factor decreases; The multiplication of the equalization adjustment coefficient J with the adjustment factor achieves a comprehensive adjustment of contrast and brightness.

5. The digestive endoscopy image enhancement analysis system according to claim 4, characterized in that: Based on the dynamic range compression range value DF and the enhanced image brightness range value DF enh Furthermore, the dynamic range compression range value DF is not equal to the enhanced image brightness range value DF. enh In this case, the enhancement adjustment module is used for intelligent adjustment, and the endoscopy images are enhanced according to the result value of the dynamic range compression range value DF. The specific processing includes: If the dynamic range compression range value DF is lower than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Intelligent enlargement; If the dynamic range compression range value DF is higher than the enhanced image brightness range value DF enh The enhanced image brightness range value DF enh Perform intelligent downsizing; iterate until the dynamic range compression range value DF equals the enhanced image brightness range value DF. enh The situation.

6. The digestive endoscopy image enhancement analysis system according to claim 1, characterized in that: The image acquisition module uses equipment including a digestive endoscopy device, the transmission module uses equipment including a data transmission device, the storage module uses equipment including a storage device, the enhancement and adjustment module uses equipment including an image enhancement and analysis system, and the output module uses equipment including a display device.

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