355 results about "Gamma correction" patented technology

The invention discloses a keyboard key defect detection method and system based on machine vision, and the method comprises the following steps: collecting the multi-modal data of keyboard keys, and carrying out the fusion preprocessing of reflection component separation, dynamic gamma correction and multi-modal constraint alignment; constructing a double-flow deep neural network for defect detection, wherein the first branch network adopts an improved U-Net architecture embedded with a CBAM attention module to extract texture features; the second branch network adopts a PointNet + + architecture to process three-dimensional geometrical characteristics; realizing cross-modal feature association through a feature fusion layer, wherein a fusion weight is adaptively adjusted according to a material type; and outputting a detection result based on the cascade classifier, wherein the method comprises the following steps: positioning a suspected defect area by a first-stage YOLOv5 network; the second-stage ResNet50 network is used for completing defect classification; and the dynamic threshold segmentation algorithm adjusts the judgment boundary according to the material type. According to the invention, high-precision and high-efficiency detection of keyboard key defects is realized.

The invention relates to the technical field of organic display data processing, in particular to an organic display interface multichannel signal modulation method, which comprises the following steps of: generating a standardized data set through gamma correction and gamut mapping, and inputting the standardized data set into time sequence, amplitude and frequency sub-models deployed in a federal learning framework; and respectively analyzing the pulse width-refresh rate relevance, the driving voltage-brightness nonlinear relationship and the conduction period-mobility dynamic response, generating an optimized weight coefficient, and fusing the optimized weight coefficient into a boundary constraint condition of a cross-sub-model. A migration rate parameter index, amplitude gradient calculation and duty ratio correction service of chain calling is constructed based on a service grid, a reinforcement learning dynamic routing strategy is combined to avoid resource competition nodes, and brightness error characteristics are periodically fed back to reversely optimize gradient updating. According to the method, the conflict between time sequence deviation accumulation and local optimum is effectively suppressed, the color gradation continuity, the dynamic range and the edge sharpness are improved, and efficient cooperative modulation of multi-channel signals is realized.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

The invention relates to the technical field of road defect detection, in particular to a road pit detection method and system, and the method comprises the following steps: S1, obtaining an original image flow of a road surface through a vehicle-mounted multispectral camera, and carrying out the motion artifact elimination to generate a vibration compensation image; s2, generating a shadow suppression image through asymmetric gamma correction; s3, adopting a dual-threshold connected domain analysis method to extract candidate pothole regions; s4, when the contrast difference value exceeds a texture mutation threshold value, determining that the area is a surface damaged area; s5, extracting a continuous pixel cluster statistical area proportion, and when the proportion is greater than 60%, determining that the feature is an effective pothole feature; and S6, calculating the minimum enclosing ellipse eccentricity rate and the ellipse area ratio, and generating a final pothole detection report. According to the method, through a multi-source information fusion and multi-stage feature extraction method, high-precision identification and standardized output of the pothole area in a complex road environment are realized, and the detection accuracy and the application reliability are improved.

The invention discloses an image enhancement method and system based on detail sensitivity and noise suppression fusion, and relates to the technical field of image processing, and the method comprises the steps: firstly processing an input unmarked microscopic cell image through a three-branch network containing detail sensitivity enhancement, a multi-scale Haar wavelet down-sampling module and loss function constraint; the network extracts multi-scale gradient features and separates illumination, reflection and noise components in a frequency domain. And then noise is subtracted from the original image, illumination influence is removed, and a noiseless reflection image is obtained. And then, Gamma correction is adopted to optimize the illumination component, and the Retinex method and the reflection image are fused to enhance the image quality. And finally, constraining the result in combination with the detail sensitive loss and the noise suppression loss to obtain high-quality output. According to the method, the three-branch image decomposition network is combined with detail enhancement, frequency domain component separation and loss constraint optimization technologies, so that high-quality enhancement and noise suppression of the unmarked microscopic cell image are realized.

The invention relates to a color calibration method and system based on a display driving chip, and the method comprises the following steps: carrying out the sub-pixel spectrum reconstruction of an original spectrum data stream based on a color gamut boundary vector group, and obtaining a sub-pixel spectrum matrix; performing white point chromaticity estimation on the sub-pixel spectrum matrix to obtain white point chromaticity coordinates; performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driving chip to drive sub-pixels of each pixel unit in the display panel to emit light to obtain color calibration display output, the defects of a traditional display driving chip in the aspect of color calibration are overcome, and the defects are mainly embodied in the technical problems that color differences between different display panels cannot be effectively processed and the color drift phenomenon occurs along with time lapse.

The invention provides a low-illumination track foreign matter intrusion detection method based on improved YOLOv8n, and the method comprises the steps: carrying out the contrast enhancement, gamma correction and image sharpening of a low-illumination image through an adaptive enhancement module, improving the quality of an input image, and improving the precision of subsequent target detection; an SPPF module is replaced with an RWSPPF module in the YOLOv8n model, and a spatial perception attention mechanism is introduced, so that the multi-scale adaptive capacity and the feature extraction effect of the model are enhanced, and the foreign matter detection accuracy is improved; and an improved loss function and an Adam optimizer are adopted, so that the training efficiency and precision are improved. By calculating the intersection-to-union ratio and the shortest distance of the foreign matter and the track area, the danger degree of the foreign matter is evaluated, safety alarm information is generated in real time, and railway traffic safety is guaranteed. On the basis of the detection method, the track foreign matter can be accurately recognized under the low-illumination condition, the detection efficiency and safety are improved, and the method has wide application prospects.

The invention provides a dark image preprocessing method and device based on machine vision, equipment and a medium, and relates to the technical field of agricultural automation, and the method comprises the steps: constructing a multi-scale Retinex enhanced fusion adaptive gamma correction illumination compensation model, carrying out the processing of an original garlic seed screening image through the illumination compensation model, eliminating illumination unevenness and light reflection interference, and obtaining an original garlic seed screening image; obtaining a first garlic seed screening image; according to a texture complexity detection method based on a gray-scale guide map, designing an adaptive joint bilateral filter, and processing the first garlic seed screening image through the adaptive joint bilateral filter to obtain a second garlic seed screening image; and performing multi-resolution contrast enhancement processing on the second garlic seed screening image through a pyramid decomposition and entropy-driven partitioning strategy to obtain a preprocessed garlic seed screening image. According to the method, the defect detection accuracy and the classification efficiency can be effectively improved under the conditions of high noise and complex backgrounds, and robust visual preprocessing support is provided for subsequent automatic sorting.

The invention discloses an underwater image enhancement method based on color channel unit compensation amount, and is applied to the technical field of underwater image processing. Comprising the following steps: respectively compensating and correcting color cast of attenuated red, green and blue channels of an underwater image by using a color channel compensation method of unit compensation dosage to obtain corrected images of the three channels; carrying out adaptive platform histogram equalization on the corrected image to realize gray scale extension and redistribution of pixel values; for a brightness channel, using a CLAHE algorithm to improve image contrast, using a GUM algorithm to enhance details, and using a Gamma correction algorithm to improve local brightness; and performing multi-scale fusion to obtain a final output enhanced image. According to the method, the quality of the underwater image is remarkably improved, the method has important practical significance in the application fields of underwater target tracking, recognition, positioning and the like, and more reliable visual support can be provided for ocean resource development and scientific research.

The invention provides an infrared image enhancement method based on CLAHE. The infrared image enhancement method comprises the following steps: step 1, obtaining an original image I; 2, processing the original image I to obtain a Gaussian smooth layer and a Gaussian detail layer; step 3, performing histogram statistics on the Gaussian smooth layer, correcting a histogram to retain an effective gray level, and performing histogram mapping; step 4, performing gamma correction on the mapped image; step 5, carrying out four-block processing on the image after gamma correction, then carrying out statistics on a histogram of each block, and then correcting each histogram; step 6, carrying out platform histogram equalization on the Gaussian detail layer to obtain a detail enhancement graph; step 7, adding the detail enhancement image and the dimming smooth image to obtain # imgabs0 #, and performing anti-overflow processing on the # imgabs1 #; according to the infrared image enhancement method based on the CLAHE, the comfort level of human eyes is improved, the contrast ratio of an image area is improved, image details are enhanced, and the image sharpness is improved.

The invention discloses an image enhancement method based on multi-level contrast and dark detail optimization. The method comprises the following steps: carrying out local gray level distribution adjustment on an infrared image; afterwards, dark part details are enhanced through a nonlinear adjustment function, and Gaussian kernels of different scales are adopted to perform multi-scale convolution operation, so that the brightness and detail performance of a dark part region are improved; calculating the global brightness distribution characteristic of the image, and dynamically adjusting the gamma value to change the gray value distribution curve of the image, thereby achieving the comprehensive optimization of the brightness and contrast of the image. And finally, guiding filtering and L0 detail filtering technologies are combined, so that the noise influence is effectively reduced, and meanwhile, the edge features are enhanced. According to the method, noise reduction and edge feature enhancement are carried out on the image based on block histogram equalization, dark region enhancement, adaptive gamma correction, guide filtering and L0 detail filtering, the definition and texture expression of the image are improved, and more reliable visual information support is provided for subsequent infrared target detection and recognition tasks.

The invention discloses an ultra-low illumination tobacco leaf image enhancement method and system based on multi-scale fusion, and belongs to the technical field of tobacco leaf image enhancement. The method comprises the following steps: S1, acquiring extremely low illumination tobacco leaf image data; s2, splitting the colored ultra-low illumination tobacco leaf image into R, G and B color channels; s3, enabling the fuzzy region to be clearer through a histogram equalization method, and obtaining a first image; s4, adapting to different illumination conditions by using a dual Gamma correction algorithm, and improving visual consistency to obtain a second image; s5, using an improved CLAHE algorithm to retain channels and collaterals of the flue-curing barn tobacco leaf image, and improving the image quality to obtain a third image; and S6, using a detail enhancement algorithm to improve specific area details of the curing barn tobacco leaf image, and retaining detail features to finally obtain a curing barn tobacco leaf curing enhancement image. Through the combination of various algorithms, the tobacco leaf image can be effectively enhanced even under the extremely low illumination conditions of serious degradation such as low signal-to-noise ratio, detail information loss and color distortion.

The invention discloses a citrus defect detection method based on pedicel influence, which comprises the following steps of: calibrating through a binocular horizontal moving camera, acquiring accurate geometric parameters of the camera, processing a binocular image by adopting an image registration and fusion technology, and extracting a pedicel contour through means such as color space conversion, graying and morphological processing. Depth information is obtained by using a three-dimensional reconstruction technology, and an accurate pedicel image is obtained by applying Otus binarization filtering and morphological closed operation; according to the method, a self-adaptive Gamma correction algorithm based on orange multi-scale texture fusion and asymmetric brightness diffusion characteristics is improved, a defect and pedicel mixed region is corrected, a pedicel image is removed through a mask method, a defect region is highlighted, finally, an active contour model and a Green formula are utilized, the orange area and the defect area are calculated respectively, and finally, the defect area is calculated. And comparing with a set value to realize accurate citrus defect detection.

The invention discloses an underwater image enhancement method and computer equipment, and the method mainly comprises the steps: correcting the color of an underwater original image through employing a self-adaptive color compensation algorithm, enhancing the details through employing a Multiply Contour Bougie (MCB) morphological detail enhancement algorithm, and achieving the global enhancement through employing a self-adaptive gamma correction method. And finally, fusing the detail and global enhancement result through a weighted stationary wavelet visual perception fusion strategy to obtain a final underwater image. The minimum color loss (MCL) algorithm designed by the invention can solve the problem of color channel information loss, so that the mean value of each color channel is similar to the histogram distribution; the image contour can be restored based on an MCB morphological detail enhancement algorithm, and details are highlighted; according to the weighted stationary wavelet visual perception fusion strategy, image information of different scales can be integrated, the definition and the layering sense are improved, the defects of a traditional method in detail and noise processing are overcome, and a high-quality image is obtained.

The invention discloses an image enhancement and visual SLAM (Simultaneous Localization and Mapping) method and system aiming at a low-illumination scene. The method comprises the following steps: firstly, judging by utilizing an image brightness detection algorithm, and carrying out image brightness adjustment on an image with abnormal current brightness; then carrying out image brightness adjustment based on a mean value adaptive Gamma value, calculating Gamma correction indexes corresponding to different low-illumination images with abnormal brightness, and carrying out brightness correction on the different low-illumination images according to the Gamma correction indexes; and finally, carrying out image contrast adjustment based on a CLAHE algorithm, wherein the CLAHE algorithm is adopted to enhance the contrast of the image after the mean value self-adaptive Gamma correction. According to the method, the problems of poor positioning precision, low robustness, poor map quality and the like of a visual SLAM system in a low-light environment in the prior art are solved. According to the method, deep learning and a traditional visual SLAM framework are combined, image performance is changed through an image enhancement method to improve image quality, and a feature point extraction mode and matching precision in a low-light environment are improved.

The invention provides a perception-consistent underwater image enhancement method, and relates to the technical field of image enhancement. The method comprises the following steps: firstly, performing dual-channel color compensation, white balance correction and histogram contrast enhancement on an original image to obtain a color enhanced image; secondly, performing median filtering and gamma correction on a brightness channel to generate a multi-exposure image sequence; extracting a detail layer and a base layer of each exposure image, performing weighted fusion based on contrast, exposure and saturation weights, and realizing detail enhancement in combination with rapid steering filtering; and finally, fusing the color enhanced image and the detail enhanced image according to the contrast ratio, the saturation degree and the structural saliency. According to the method, the image quality is improved through dual-path cooperation and dynamic exposure fusion, the method is suitable for scenes such as underwater monitoring, aquatic product monitoring and underwater inspection, complex training is not needed, and deployment on low-power-consumption equipment is facilitated.

The invention provides a processing method and device of a spliced display screen and a spliced display device, and belongs to the technical field of display. The spliced display screen comprises a plurality of sub-display screens spliced together, and the processing method of the spliced display screen comprises the steps that all the sub-display screens are controlled to display a full-white picture, and RGB data of each sub-display screen are detected; converting the RGB data into brightness and color coordinates of the sub display screens; sequentially performing color temperature correction, gamma correction and brightness correction on each sub-display screen according to the brightness and the color coordinates of the plurality of sub-display screens, so that the difference value between the color temperature of each sub-display screen and a target color temperature is not greater than a preset first threshold value, the difference value between the maximum brightness of each sub-display screen and the target brightness is not greater than a preset second threshold value. The display uniformity of the spliced display screen can be improved.

The invention relates to the field of tea disease detection, in particular to a tea disease detection method based on a lightweight YOLOv8 model. According to the technical scheme, the method comprises the steps of collecting various types of tea disease images; the method comprises the following steps: preprocessing multiple types of collected tea disease images, including illumination normalization, geometric enhancement, illumination enhancement, adaptive histogram equalization, a multi-scale Retinex algorithm, dynamic Gamma correction and image noise removal, and marking scab regions in different types of tea disease images to obtain a training data set; a lightweight YOLOv8 model is constructed; using the training data set to train the lightweight YOLOv8 model; and inputting a collected tea disease image into the trained lightweight YOLOv8 model, and outputting a tea disease type through the trained lightweight YOLOv8 model. The method is suitable for tea disease detection.

The invention discloses an SLAM front-end optimization method based on brightness grading and gradient constraint, and relates to the technical field of computer vision and vision SLAM. In order to solve the defects that adaptive grading enhancement and parameterization control based on illumination types are lacked in the prior art, and feature point quality, spatial distribution uniformity and front-end real-time performance are difficult to consider in a high-frequency input scene, the invention provides a comprehensive brightness grading and feature optimization scheme. The method comprises the following steps: firstly, calculating the average brightness of an input image, dividing the image into a dark light type, a normal type and an overexposure type, and respectively adopting gamma correction, contrast limited adaptive histogram equalization and inversion enhancement strategies for different types to realize illumination adaptive enhancement; then screening high-quality feature points with significant local curvature changes; and updating the detection area. According to the method, the feature stability, the matching precision and the real-time performance of the SLAM front end in a complex indoor environment are remarkably improved, and the method is suitable for a self-localization and mapping system.

The invention relates to the technical field of image enhancement, in particular to a low-illumination image enhancement method and system for parking lot monitoring. The method comprises the following steps: acquiring an enhancement coefficient according to the brightness difference between a low-illumination area and an adjacent illumination transition area, the brightness change rate in the illumination transition area along the irradiation direction of the vehicle lamp and the length in the irradiation direction of the vehicle lamp; according to the difference between the brightness value of the pixel point in the illumination transition area and the brightness value of the projection point of the pixel point on the edge of the adjacent low-illumination area along the vehicle lamp irradiation direction enhanced by using the enhancement coefficient, and the brightness change rate of the pixel point in the vehicle lamp irradiation direction, obtaining the compression strength; and determining a first gamma value of the low-illumination area and a second gamma value of the illumination transition area, and enhancing the monitoring image to obtain a monitoring enhanced image. According to the invention, adaptive gamma correction is carried out on the illumination transition region and the low-illumination region, the overall continuity of the image is enhanced, and the enhancement effect of the monitoring image is improved.

The invention discloses a human face mouth shape coordination generation method based on geometric constraints, and relates to the technical field of image generation, and the method comprises the steps: 1, synchronously generating face vertex index tables corresponding to mouth opening and closing spatial form matrixes one by one; 2, based on the facial vertex index table, mapping the lip and gum combined boundary constraint set to a unified coordinate system to form a geometric prior constraint tensor; 3, performing multi-modal topological cascade reversible mixed transformation on the geometric prior constraint tensor to obtain a high-dimensional tooth texture candidate tensor and constraint consistency confidence spectrum; and step 4, using the high-dimensional tooth texture candidate tensor and the constraint consistency confidence spectrum as input, completing anti-aliasing, illumination compensation and gamma correction on a unified rendering pipeline, and finally obtaining a mouth shape coordination synthetic frame sequence. According to the method, the sense of reality, geometric consistency and time sequence coherence of tooth textures in the mouth shape synthesis process are effectively improved, and the problem that lips and teeth are not coordinated in an existing method is solved.

The invention discloses an image enhancement method and system based on brightness correction and fusion channel prior, which relates to the field of image enhancement technology. First, the image to be enhanced is converted from RGB space to HSV space; then the V channel image is brightness corrected by gamma correction; the brightness-corrected V channel image, H channel image and S channel image are merged, and converted back from HSV space to RGB space to obtain a brightness-corrected image in RGB space; for the brightness-corrected image in RGB space, its G channel image and B channel image are channel-fused and enhanced, and merged with the R channel image to obtain a final enhanced image. The invention can enhance the contrast of the image, retain the detailed information of the image, and enable the enhanced image to maintain the naturalness of the image.

The invention discloses an optical compensation method for a display panel, which comprises the following steps of: shooting a gray scale test chart of the display panel at different exposure time, acquiring corresponding gray scale data, and establishing a quadratic polynomial fitting model of the exposure time and a gray scale value; and then, in combination with a Gamma correction principle, the Gamma value of the display panel is reversely deduced based on the fitting model, so that accurate mapping between the gray scale and the actual brightness is realized, the nonlinear influence of the display system is eliminated, the accuracy and reliability of a compensation algorithm are ensured, and the overall brightness reduction effect is further improved. And finally, combining the correction result, dividing the display panel into a plurality of grid areas, counting an average gray value of each grid, calculating a local compensation amount, generating a compensation lookup table (LUT), and applying the compensation lookup table to a real-time display compensation process. The display uniformity, the brightness consistency and the terminal visual effect of the panel can be improved, and good adaptability and practicability are achieved.

The invention discloses an image enhancement method and system based on a gamma correction prior driven attention mechanism, and relates to the field of optics, and the method comprises the following steps: constructing a low-light image enhancement neural network based on a pre-configured network structure; based on a pre-selected loss function, a training data set and an evaluation index, training and evaluating the low-light image enhancement neural network; and based on the trained low-light image enhancement neural network, sequentially carrying out ablation experiments on the number of the extended gamma correction attention modules and the number of the channel prior modules and the loss function to obtain an enhanced image. The invention proposes a new model as an attention mechanism based on a theoretical formula, proposes a stacked and modularized attention module to pay attention to details of the image, and the enhancement enables the weight to be light but the performance to be advanced, and maintains high efficiency and stable operation.

The invention discloses a self-adaptive low-illumination image enhancement method and device and a storage medium, which are used for stably outputting a high-quality image under various complex conditions. The method comprises the steps of generating an adaptive key parameter set through a preset mapping function based on brightness features; processing the brightness channel through a combined filtering strategy and carrying out adaptive weighted fusion to obtain an irradiation component; converting the brightness channel and the irradiation component to a logarithm domain and performing operation to obtain an initial reflection component; performing linear stretching processing on the initial reflection component to obtain an enhanced reflection component; performing nonlinear gamma correction processing on the irradiation component to obtain a corrected irradiation component; converting the enhanced reflection component back to a linear domain, and performing operation with the corrected irradiation component to obtain an enhanced brightness channel; and recombining the enhanced brightness channel with the original hue channel and saturation channel in the HSV color space, and converting back to the RGB format to obtain an enhanced image.

The invention relates to the technical field of display screen defect visual inspection, in particular to an LED display screen defect visual inspection method and system, and the method comprises the following steps: collecting an image of an LED display screen; performing gamma correction and noise suppression on the image of the LED display screen to obtain a preprocessed image; local contrast enhancement and adaptive threshold segmentation are applied to the preprocessed image, and candidate abnormal areas are identified; for the candidate abnormal region, extracting at least one dimension feature which comprises at least one of brightness, color, texture, edge gradient and spectral response; processing the at least one dimension feature based on a preset discrimination rule so as to distinguish real microdefects, optical artifacts and digital artifacts of the LED display screen; and outputting the type, the position and the confidence of the defect obtained by distinguishing. The detection accuracy and reliability are improved.

The in-situ measurement method for the depth and the width of the thick plate aluminum alloy perforation welding small hole molten pool is characterized by comprising the following steps: 1, image processing: preprocessing an obtained image, removing random splashing influence through combination of CLAHE and Gamma correction, improving the image quality, enhancing the edge characteristics of the molten pool and improving the welding quality detection accuracy; 2, deep learning: improving the accuracy and stability of small hole molten pool edge identification by improving the direction perception position code of SegFormer and introducing a time sequence information fusion mechanism; the method has the beneficial effects that in-situ real-time measurement of the depth and the width of the small-hole molten pool is realized through an image processing and deep learning fusion method according to the real-time detection requirement of key geometric feature changes of the small-hole molten pool in the variable polarity plasma arc welding perforation process of the thick plate aluminum alloy.

The invention provides a digital pathological section color calibration method and device, and the method comprises the following steps: obtaining a standard color image and an image to be subjected to color correction, and extracting the feature points of the standard color image and the feature points of the image to be subjected to color correction, matching the standard color image and the image to be subjected to color correction based on the feature points to obtain a standard color matching image and a matching image to be subjected to color correction; performing histogram color correction on the matching image to be subjected to color correction by taking a standard color matching image as a reference to obtain a first color correction image, and performing CCM color correction on the first color correction image by taking the standard color matching image as the reference to obtain a second color correction image, and performing gamma correction on the color of the second color correction image by taking a standard color matching image as a reference to obtain a color correction completed image. According to the scheme, all pixel information of the target image is fully utilized, the color correction process is quantified to eliminate artificial errors, and the efficiency and accuracy of color correction of the digital pathological image can be remarkably improved.

The invention provides a chromosome image enhancement method and system based on semantic guidance. The method comprises the following steps: S1, preprocessing; s2, outputting a deep-band probability graph, a grey-band probability graph and a shallow-band probability graph through a semantic segmentation network composed of a lightweight encoder and a multi-scale decoder; s3, implementing differential layered enhancement according to a band type: adopting local adaptive histogram equalization for a deep band, adopting central axis constraint bilateral filtering for a gray band, adopting dynamic threshold truncation and gamma correction for a shallow band, and performing weighted fusion for a transition region according to probability; s4, carrying out structure strengthening, wherein the structure strengthening comprises centromere local sharpening, stripe phase alignment, edge sensing super-resolution and overlapping region separation; and S5, performing quality evaluation based on the deep band integrity, the band stripe contrast uniformity, the SSIM and the noise density, and triggering adaptive re-enhancement if necessary. According to the scheme, the contrast ratio and details are remarkably improved while the stripe structure and the position relation are kept, and the method has the advantages of light weight, interpretability and cross-sample robustness and is suitable for being integrated into an automatic karyotype analysis process.

The invention discloses a low-illumination blurred image 3D scene reconstruction method based on Gaussian sputtering, and the method comprises the steps: S1, building a progressive iteration enhancement frame, and setting a middle brightness anchor point between low-light observation and target brightness; s2, generating a plurality of enhanced images based on the intermediate brightness anchor points in combination with histogram equalization and gamma correction technologies; s3, carrying out rapid deblurring processing on the enhanced image; s4, constructing a scene representation model based on 3D Gaussian sputtering, and performing explicit estimation and noise suppression in combination with a noise sensing module; s5, taking the reconstructed rendered image as the deblurring prior of the enhanced image of the next brightness level so as to execute deblurring processing operation, and performing iterative optimization until the target brightness is reached; and S6, generating a high-quality new view angle image based on the finally reconstructed 3D scene. According to the method, the rendering speed is greatly improved while the reconstruction quality is ensured, real-time three-dimensional reconstruction is realized, and the problem of noise amplification in a low-light environment is effectively solved.