Signal processing system and method based on image sensor

Abstract

The invention belongs to the technical field of image signal processing, and discloses a signal processing system and method based on an image sensor, and the system comprises an image collection module, a preprocessing module, a noise suppression module, a detail enhancement module, a dynamic range adjustment module, a color correction module, and an output module. According to the invention, a mode of cooperative work of a plurality of functional modules is adopted, each module is optimally designed for a specific image processing task, comprehensive processing of image signals is realized through data interaction and cooperative processing among the modules, and the processing effect and efficiency are improved. The noise level can be automatically estimated according to the local features of the image, a corresponding suppression strategy is adopted, and the detail information of the image is reserved while the noise is effectively removed. The detail information in the image can be enhanced in a targeted manner according to the sensitivity of the human eyes to different frequency components, so that the enhanced image better conforms to the visual perception of the human eyes.

Description

Technical Field

[0001] This invention belongs to the field of image signal processing technology, and in particular relates to a signal processing system and method based on an image sensor. Background Technology

[0002] With the continuous development of image sensor technology, it has been widely used in many fields. However, during the process of acquiring image signals, image sensors are inevitably affected by a variety of factors, such as the sensor's own thermal noise, photon shot noise, and circuit interference noise. These noises can reduce image quality, leading to problems such as image blurring, loss of detail, and decreased contrast, which seriously affect subsequent image analysis and processing.

[0003] Traditional image signal processing methods often only target the removal of single types of noise, lacking the ability to comprehensively process multiple types of noise, and their processing effects are limited, making it difficult to meet the needs of high-precision image processing. Therefore, developing an efficient and comprehensive signal processing system based on image sensors is of significant practical importance. Based on the above analysis, the problems and shortcomings of existing technologies are as follows:

[0004] Traditional image signal processing methods often only remove single types of noise, lack the ability to process multiple types of noise comprehensively, and have limited processing effects, making it difficult to meet the needs of high-precision image processing. Summary of the Invention

[0005] To address the problems existing in the prior art, the present invention provides a signal processing system and method based on an image sensor.

[0006] This invention is implemented as follows: a signal processing system based on an image sensor includes:

[0007] Image acquisition module, preprocessing module, noise suppression module, detail enhancement module, dynamic range adjustment module, color correction module, output module;

[0008] The image acquisition module, connected to the preprocessing module, is responsible for communicating with the image sensor, acquiring raw image signals, and converting them into digital signals for transmission to subsequent modules for processing.

[0009] The preprocessing module, connected to the noise suppression module, is used to perform preliminary processing on the acquired raw image data, including depigmentation and white balance correction.

[0010] The noise suppression module, connected to the detail enhancement module, is used to perform noise suppression processing on the preprocessed image data using an adaptive noise estimation and suppression algorithm, effectively removing thermal noise, photon shot noise and circuit interference noise from the image.

[0011] The detail enhancement module, connected to the dynamic range adjustment module, color correction module, and output module, is used to enhance the details of noise-suppressed image data based on the characteristics of human visual perception.

[0012] The dynamic range adjustment module, connected to the detail enhancement module, is used to automatically adjust the dynamic range of the image according to the actual situation of the image, so that the details in both the bright and dark parts of the image can be better presented.

[0013] The color correction module, connected to the detail enhancement module, is used to perform color correction on the image data after dynamic range adjustment to eliminate color deviation.

[0014] The output module, connected to the detail enhancement module, is used to convert and encode the color-corrected image data to output an image signal that conforms to a specific standard.

[0015] Furthermore, the image acquisition module:

[0016] The image sensor converts light signals into electrical signals, and the analog-to-digital converter (ADC) converts the analog electrical signals into digital signals. The image acquisition module transmits data with the image sensor and ADC through a specific interface protocol to ensure the accurate acquisition of raw image data.

[0017] Furthermore, the preprocessing module:

[0018] De-mosaic algorithm:

[0019] Mathematical formula: Assume the original image data is ,in Represents the coordinates of pixels; the image data after de-mosaic is The bilinear interpolation algorithm is used for de-mosaic processing, and its formula is as follows:

[0020]

[0021] in, For the original image in coordinates Pixel value at; To the image after depixelation in coordinates Pixel value at; These are the interpolation weights, typically ranging from [0,1], and satisfying... .

[0022] Furthermore, the noise suppression module:

[0023] Noise suppression algorithm:

[0024] Mathematical formula: Noise suppression is achieved using a wavelet thresholding denoising algorithm; let the coefficients after wavelet transform be... The coefficient after thresholding is Then we have:

[0025]

[0026] in, This is the threshold adjustment coefficient. The threshold value is calculated using the following formula:

[0027]

[0028] It is a symbolic function;

[0029] These are the coefficients after wavelet transform; These are the coefficients after thresholding. is the threshold adjustment coefficient, whose value range is usually [0.5, 1.5]; T is the threshold; denoted as the noise standard deviation; N is the total number of pixels in the image.

[0030] Furthermore, the dynamic range adjustment module:

[0031] Dynamic range adjustment algorithm:

[0032] Mathematical formula: Dynamic range adjustment is performed using a histogram equalization algorithm; let the gray levels of the original image be r. k Its probability of occurrence is p(r) k The equalized gray level is s k Then we have:

[0033]

[0034] in, The gray level of the image;

[0035] r k p(r) represents the gray levels of the original image. k ) represents the grayscale level r k The probability of occurrence; s k The equalized gray levels; T(r) k ) is the grayscale transformation function; k is the index of the grayscale level; L is the number of grayscale levels in the image.

[0036] Furthermore, the color correction module:

[0037] Color correction algorithm:

[0038] Mathematical formula: A 3×3 color correction matrix (M) is used to transform the RGB channel values ​​of the image; let the RGB channel values ​​of the original image be... The corrected channel value is Then we have:

[0039]

[0040] Among them, the elements (m_ij) of the color correction matrix (M) are adjusted according to the color deviation of the image;

[0041] Parameter meaning: These are the RGB channel values ​​of the original image; These are the corrected RGB channel values; It is a 3×3 color correction matrix; For elements of the color correction matrix, .

[0042] Another object of the present invention is to provide a signal processing method based on an image sensor, comprising:

[0043] Step 1: The image acquisition module is responsible for communicating with the image sensor, acquiring the raw image signal, and converting it into a digital signal for transmission to subsequent modules for processing.

[0044] Step 2: The acquired raw image data is pre-processed using the preprocessing module, including de-mosaicing and white balance correction.

[0045] Step 3: The noise suppression module uses an adaptive noise estimation and suppression algorithm to perform noise suppression processing on the preprocessed image data, effectively removing thermal noise, photon shot noise and circuit interference noise from the image.

[0046] Step 4: Based on the characteristics of human visual perception, the detail enhancement module performs detail enhancement processing on the noise-suppressed image data.

[0047] Step 5: The dynamic range adjustment module automatically adjusts the dynamic range of the image according to the actual situation of the image, so that the details in both the bright and dark parts of the image can be better presented.

[0048] Step 6: Perform color correction on the image data after dynamic range adjustment using the color correction module to eliminate color deviation;

[0049] Step 7: The color-corrected image data is converted and encoded by the output module to output an image signal that conforms to a specific standard.

[0050] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the image sensor-based signal processing method.

[0051] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the image sensor-based signal processing method.

[0052] Another object of the present invention is to provide an information data processing terminal for implementing the image sensor-based signal processing system.

[0053] Based on the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by this invention from the following aspects:

[0054] First, addressing the technical problems existing in the prior art and the difficulty in solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0055] 1. Multi-module collaborative processing: This system adopts a collaborative approach with multiple functional modules. Each module is optimized for specific image processing tasks. Through data interaction and collaborative processing between modules, comprehensive processing of image signals is achieved, improving processing effect and efficiency.

[0056] 2. Adaptive noise estimation and suppression algorithm: An adaptive noise estimation and suppression algorithm is proposed. This algorithm can automatically estimate the noise level based on the local features of the image and adopt the corresponding suppression strategy to effectively remove noise while preserving the details of the image.

[0057] 3. Detail enhancement method based on human visual characteristics: Combining the characteristics of human vision, a detail enhancement method based on human visual characteristics is designed. This method can enhance the detail information in the image in a targeted manner according to the sensitivity of the human eye to different frequency components, so that the enhanced image is more in line with the visual perception of the human eye.

[0058] Secondly, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0059] Does the technical solution of this invention overcome technical bias?

[0060] Noise suppression: It can simultaneously suppress multiple noise types generated by the image sensor, such as thermal noise, photon shot noise, and circuit interference noise, significantly reducing the noise level in the image.

[0061] Detail enhancement: Enhances the details in an image, making edges sharper, textures richer, and improving the visual effect.

[0062] Dynamic range adjustment: Automatically adjusts the dynamic range of the image based on its actual condition, so that details in both bright and dark areas of the image can be better presented.

[0063] Color correction: Corrects the colors of an image to eliminate color deviations and make the colors of the image more realistic and natural. Attached Figure Description

[0064] Figure 1 This is a block diagram of a signal processing system based on an image sensor provided in an embodiment of the present invention.

[0065] Figure 2 This is a flowchart of the image acquisition module method provided in an embodiment of the present invention.

[0066] Figure 3 This is a flowchart of the preprocessing module method provided in an embodiment of the present invention.

[0067] Figure 4 This is a flowchart of a signal processing method based on an image sensor provided in an embodiment of the present invention.

[0068] Figure 1 The module consists of: 1. Image acquisition module; 2. Preprocessing module; 3. Noise suppression module; 4. Detail enhancement module; 5. Dynamic range adjustment module; 6. Color correction module; and 7. Output module. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0070] like Figure 1 As shown, an embodiment of the present invention provides a signal processing system based on an image sensor, comprising:

[0071] Image acquisition module 1, preprocessing module 2, noise suppression module 3, detail enhancement module 4, dynamic range adjustment module 5, color correction module 6, output module 7;

[0072] Image acquisition module 1, connected to preprocessing module 2, is responsible for communicating with the image sensor, acquiring raw image signals, and converting them into digital signals for transmission to subsequent modules for processing.

[0073] The preprocessing module 2, connected to the noise suppression module 3, is used to perform preliminary processing on the acquired raw image data, including depigmentation and white balance correction.

[0074] The noise suppression module 3, connected to the detail enhancement module 4, is used to perform noise suppression processing on the preprocessed image data using an adaptive noise estimation and suppression algorithm, effectively removing thermal noise, photon shot noise and circuit interference noise from the image.

[0075] The detail enhancement module 4, connected to the dynamic range adjustment module 5, the color correction module 6, and the output module 7, is used to perform detail enhancement processing on the noise-suppressed image data based on the characteristics of human visual perception.

[0076] The dynamic range adjustment module 5, connected to the detail enhancement module 4, is used to automatically adjust the dynamic range of the image according to the actual situation of the image, so that the details in both the bright and dark parts of the image can be better presented.

[0077] The color correction module 6, connected to the detail enhancement module 4, is used to perform color correction on the image data after dynamic range adjustment to eliminate color deviation.

[0078] Output module 7, connected to detail enhancement module 4, is used to perform format conversion and encoding processing on the color-corrected image data and output image signals that conform to specific standards.

[0079] like Figure 2 As shown, the image acquisition module provided in this embodiment of the invention:

[0080] S101, the image sensor converts light signals into electrical signals, and then the analog electrical signals are converted into digital signals by an analog-to-digital converter (ADC);

[0081] S102, the image acquisition module transmits data with the image sensor and ADC through a specific interface protocol to ensure accurate acquisition of raw image data.

[0082] like Figure 3 As shown, the preprocessing module provided in this embodiment of the invention:

[0083] S201, Demosaic Algorithm:

[0084] Mathematical formula: Assume the original image data is ,in Represents the coordinates of pixels; the image data after de-mosaic is The bilinear interpolation algorithm is used for de-mosaic processing, and its formula is as follows:

[0085]

[0086] in, For the original image in coordinates Pixel value at; To the image after depixelation in coordinates Pixel value at; These are the interpolation weights, typically ranging from [0,1], and satisfying... .

[0087] The noise suppression module provided in this embodiment of the invention:

[0088] Noise suppression algorithm:

[0089] Mathematical formula: Noise suppression is achieved using a wavelet thresholding denoising algorithm; let the coefficients after wavelet transform be... The coefficient after thresholding is Then we have:

[0090]

[0091] in, This is the threshold adjustment coefficient. The threshold value is calculated using the following formula:

[0092]

[0093] It is a symbolic function;

[0094] These are the coefficients after wavelet transform; These are the coefficients after thresholding. is the threshold adjustment coefficient, whose value range is usually [0.5, 1.5]; T is the threshold; denoted as the noise standard deviation; N is the total number of pixels in the image.

[0095] The dynamic range adjustment module provided in this embodiment of the invention:

[0096] Dynamic range adjustment algorithm:

[0097] Mathematical formula: Dynamic range adjustment is performed using a histogram equalization algorithm; let the gray levels of the original image be r. k Its probability of occurrence is p(r) k The equalized gray level is s k Then we have:

[0098]

[0099] in, The gray level of the image;

[0100] r kp(r) represents the gray levels of the original image. k ) represents the grayscale level r k The probability of occurrence; s k The equalized gray levels; T(r) k ) is the grayscale transformation function; k is the index of the grayscale level; L is the number of grayscale levels in the image.

[0101] The color correction module provided in this embodiment of the invention:

[0102] Color correction algorithm:

[0103] Mathematical formula: A 3×3 color correction matrix (M) is used to transform the RGB channel values ​​of the image; let the RGB channel values ​​of the original image be... The corrected channel value is Then we have:

[0104]

[0105] Among them, the elements (m_ij) of the color correction matrix (M) are adjusted according to the color deviation of the image;

[0106] Parameter meaning: These are the RGB channel values ​​of the original image; These are the corrected RGB channel values; It is a 3×3 color correction matrix; For elements of the color correction matrix, .

[0107] like Figure 4 As shown, an embodiment of the present invention provides a signal processing method based on an image sensor, comprising:

[0108] S301, through the image acquisition module, is responsible for communicating with the image sensor, acquiring the raw image signal, and converting it into a digital signal for transmission to subsequent modules for processing;

[0109] S302 performs preliminary processing on the acquired raw image data through the preprocessing module, including depigmentation and white balance correction.

[0110] S303 uses an adaptive noise estimation and suppression algorithm through a noise suppression module to perform noise suppression processing on the preprocessed image data, effectively removing thermal noise, photon shot noise and circuit interference noise from the image.

[0111] S304, based on the characteristics of human visual perception, performs detail enhancement processing on the noise-suppressed image data through the detail enhancement module;

[0112] The S305 automatically adjusts the dynamic range of an image based on its actual conditions using a dynamic range adjustment module, allowing for better rendering of details in both bright and dark areas.

[0113] S306 uses a color correction module to perform color correction on the image data after dynamic range adjustment, eliminating color deviation;

[0114] The S307 uses an output module to convert and encode the color-corrected image data, outputting an image signal that conforms to a specific standard.

[0115] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the image sensor-based signal processing method.

[0116] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the image sensor-based signal processing method.

[0117] Another object of the present invention is to provide an information data processing terminal for implementing the image sensor-based signal processing system.

[0118] Specific implementation of the present invention:

[0119] (a) Hardware Implementation

[0120] The hardware components of this system mainly include an image sensor, an analog-to-digital converter (ADC), a digital signal processor (DSP) or a field-programmable gate array (FPGA), and memory. The image sensor is responsible for acquiring raw optical signals and converting them into electrical signals. The ADC converts analog electrical signals into digital signals. The DSP or FPGA serves as the core processing unit of the system, implementing the functions of the above modules. The memory is used to store the acquired image data and intermediate results during the processing.

[0121] (II) Software Implementation

[0122] In terms of software, a modular programming approach is adopted, where each functional module is written as an independent subroutine or function. The main program calls these subroutines to achieve the overall functionality of the system. The specific steps are as follows:

[0123] 1. Initialization: Initialize hardware devices such as image sensors, ADCs, DSPs or FPGAs, including sensor parameter configuration, interface initialization, memory allocation, etc.

[0124] 2. Image Acquisition: The image acquisition module acquires raw image data and stores it in the memory.

[0125] 3. Preprocessing: Call the subroutine of the preprocessing module to perform preprocessing operations such as de-mosaicing and white balance correction on the acquired raw image data.

[0126] 4. Noise Suppression: Call the subroutine of the noise suppression module and use an adaptive noise estimation and suppression algorithm to perform noise suppression processing on the preprocessed image data.

[0127] 5. Detail Enhancement: Calls the subroutine of the detail enhancement module to perform detail enhancement processing on the noise-suppressed image data based on the characteristics of human visual perception.

[0128] 6. Dynamic Range Adjustment: Calls the subroutine of the dynamic range adjustment module to automatically adjust the dynamic range of the image according to the actual situation of the image.

[0129] 7. Color Correction: Calls the subroutine of the color correction module to perform color correction on the image data after dynamic range adjustment.

[0130] 8. Output: Call the subroutine of the output module to perform format conversion and encoding processing on the color-corrected image data, and output an image signal that conforms to a specific standard.

[0131] I. Specific application areas or related products of this invention.

[0132] Healthcare

[0133] Disease Diagnosis: In the field of medical imaging diagnosis, if this invention relates to image processing algorithms, it can be used to analyze images such as X-rays, CT scans, and MRI scans, assisting doctors in more accurately detecting lesion sites, determining disease types, and severity. For example, by processing lung CT images, lung nodules can be identified more quickly and accurately, and their malignancy risk can be assessed.

[0134] Drug development: If this invention is related to bioinformatics or cheminformatics, it can be applied to drug target discovery, drug molecule design, and drug activity prediction. For example, machine learning algorithms can be used to analyze large amounts of biological data to uncover potential drug targets and accelerate the new drug development process.

[0135] Health Monitoring: In wearable devices and telemedicine, this invention can be used to collect and analyze an individual's physiological data in real time, such as heart rate, blood pressure, and blood sugar, to promptly detect health abnormalities and provide early warnings. For example, a smart bracelet, combined with the data analysis technology of this invention, can assess a user's stress level based on indicators such as heart rate variability and provide corresponding relaxation suggestions.

[0136] Transportation sector

[0137] Intelligent Transportation Systems: If this invention relates to traffic flow prediction, route planning algorithms, etc., it can be applied to urban traffic management to optimize traffic light timing and alleviate congestion. Simultaneously, it provides drivers with real-time optimal route suggestions, improving travel efficiency. For example, traffic prediction systems based on big data and artificial intelligence can predict the time and location of traffic congestion in advance and push routes to avoid congestion to users through navigation software.

[0138] Autonomous driving technology: This invention plays a crucial role in the perception, decision-making, and control systems of autonomous vehicles. For example, it processes data from sensors such as LiDAR and cameras to achieve accurate perception and recognition of the surrounding environment, thereby enabling safe driving decisions. Tesla's Autopilot driver assistance system, for instance, continuously applies new algorithms and technologies to improve its autonomous driving performance and safety.

[0139] Industrial manufacturing sector

[0140] Intelligent Manufacturing: This invention can be used to automate and intelligently control the production process, improving production efficiency and product quality. For example, machine vision technology can be used to inspect products on the production line, promptly identifying and classifying defective products. Simultaneously, data analysis algorithms can be used to analyze production data, optimizing production processes and reducing production costs.

[0141] Industrial robots: This invention provides more precise and efficient solutions for motion control, path planning, and task allocation in industrial robots. For example, it enables robots to automatically adjust their motion trajectory and operation mode according to different work tasks and environmental changes, thereby improving production flexibility and adaptability.

[0142] Related products

[0143] Medical devices, such as intelligent imaging diagnostic instruments and portable health monitoring devices, integrate the technologies of this invention and can provide more accurate and convenient services for the medical and health field.

[0144] Intelligent transportation products include intelligent traffic lights, in-vehicle navigation devices, and autonomous vehicles. The technology of this invention enables intelligent traffic management and efficient and convenient travel.

[0145] Industrial automation products, such as industrial vision inspection systems and intelligent industrial robots, have improved the quality and efficiency of industrial production through the application of this invention.

[0146] Fintech products, such as risk assessment software and quantitative trading platforms, provide financial institutions with more scientific and accurate risk assessment and trading decision support.

[0147] Smart home products, such as smart speakers, smart door locks, and smart appliances, utilize the technology of this invention to achieve intelligent control and energy management of home devices.

[0148] It should be noted that embodiments of the present invention can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by a suitable instruction execution system, such as a microprocessor or dedicated-design hardware. Those skilled in the art will understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, for example, such code provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuitry such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of the above-described hardware circuitry and software, such as firmware.

[0149] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A signal processing system based on an image sensor, characterized in that, The image sensor-based signal processing system includes: Image acquisition module, preprocessing module, noise suppression module, detail enhancement module, dynamic range adjustment module, color correction module, output module; The image acquisition module, connected to the preprocessing module, is responsible for communicating with the image sensor, acquiring raw image signals, and converting them into digital signals for transmission to subsequent modules for processing. The preprocessing module, connected to the noise suppression module, is used to perform preliminary processing on the acquired raw image data, including depigmentation and white balance correction. The noise suppression module, connected to the detail enhancement module, is used to perform noise suppression processing on the preprocessed image data using an adaptive noise estimation and suppression algorithm, effectively removing thermal noise, photon shot noise and circuit interference noise from the image. The detail enhancement module, connected to the dynamic range adjustment module, color correction module, and output module, is used to enhance the details of noise-suppressed image data based on the characteristics of human visual perception. The dynamic range adjustment module, connected to the detail enhancement module, is used to automatically adjust the dynamic range of the image according to the actual situation of the image, so that the details in both the bright and dark parts of the image can be better presented. The color correction module, connected to the detail enhancement module, is used to perform color correction on the image data after dynamic range adjustment to eliminate color deviation. The output module, connected to the detail enhancement module, is used to convert and encode the color-corrected image data to output an image signal that conforms to a specific standard.

2. The signal processing system based on an image sensor as described in claim 1, characterized in that, The image acquisition module: The image sensor converts light signals into electrical signals, and the analog-to-digital converter (ADC) converts the analog electrical signals into digital signals. The image acquisition module transmits data with the image sensor and ADC through a specific interface protocol to ensure the accurate acquisition of raw image data.

3. The signal processing system based on an image sensor as described in claim 1, characterized in that, The preprocessing module: De-mosaic algorithm: Mathematical formula: Assume the original image data is ,in Represents the coordinates of pixels; the image data after de-mosaic is The bilinear interpolation algorithm is used for de-mosaic processing, and its formula is as follows: in, For the original image in coordinates Pixel value at; To the image after depixelation in coordinates Pixel value at; These are the interpolation weights, typically ranging from [0,1], and satisfying... .

4. The signal processing system based on an image sensor as described in claim 1, characterized in that, The noise suppression module: Noise suppression algorithm: Mathematical formula: Noise suppression is achieved using a wavelet thresholding denoising algorithm; let the coefficients after wavelet transform be... The coefficient after thresholding is Then we have: in, This is the threshold adjustment coefficient. The threshold value is calculated using the following formula: It is a symbolic function; These are the coefficients after wavelet transform; These are the coefficients after thresholding. is the threshold adjustment coefficient, whose value range is usually [0.5, 1.5]; T is the threshold; denoted as the noise standard deviation; N is the total number of pixels in the image.

5. The signal processing system based on an image sensor as described in claim 1, characterized in that, The dynamic range adjustment module: Dynamic range adjustment algorithm: Mathematical formula: Dynamic range adjustment is performed using a histogram equalization algorithm; let the gray levels of the original image be r. k Its probability of occurrence is p(r) k The equalized gray level is s k Then we have: in, The gray level of the image; r k p(r) represents the gray levels of the original image. k ) represents the grayscale level r k The probability of occurrence; s k The equalized gray levels; T(r) k ) is the grayscale transformation function; k is the index of the grayscale level; L is the number of grayscale levels in the image.

6. The signal processing system based on an image sensor as described in claim 1, characterized in that, The color correction module: Color correction algorithm: Mathematical formula: A 3×3 color correction matrix (M) is used to transform the RGB channel values ​​of the image; let the RGB channel values ​​of the original image be... The corrected channel value is Then we have: Among them, the elements (m_ij) of the color correction matrix (M) are adjusted according to the color deviation of the image; Parameter meaning: These are the RGB channel values ​​of the original image; These are the corrected RGB channel values; It is a 3×3 color correction matrix; For elements of the color correction matrix, .

7. A signal processing method based on an image sensor, implementing the signal processing system based on an image sensor as described in any one of claims 1-6, characterized in that, The image sensor-based signal processing method includes: Step 1: The image acquisition module is responsible for communicating with the image sensor, acquiring the raw image signal, and converting it into a digital signal for transmission to subsequent modules for processing. Step 2: The acquired raw image data is pre-processed using the preprocessing module, including de-mosaicing and white balance correction. Step 3: The noise suppression module uses an adaptive noise estimation and suppression algorithm to perform noise suppression processing on the preprocessed image data, effectively removing thermal noise, photon shot noise and circuit interference noise from the image. Step 4: Based on the characteristics of human visual perception, the detail enhancement module performs detail enhancement processing on the noise-suppressed image data. Step 5: The dynamic range adjustment module automatically adjusts the dynamic range of the image according to the actual situation of the image, so that the details in both the bright and dark parts of the image can be better presented. Step 6: Perform color correction on the image data after dynamic range adjustment using the color correction module to eliminate color deviation; Step 7: The color-corrected image data is converted and encoded by the output module to output an image signal that conforms to a specific standard.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the signal processing method based on an image sensor as described in claim 7.

9. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the signal processing method based on an image sensor as described in claim 7.

10. An information data processing terminal, characterized in that, The information data processing terminal is used to implement the signal processing system based on the image sensor as described in any one of claims 1-6.

Images