584 results about "Contrast enhancement" patented technology

The invention relates to the technical field of image processing, and discloses an industrial part alignment method and system based on visual analysis and a storage medium. The method comprises the steps that a three-view camera collects an industrial part image, and preprocessing is carried out through gradient magnitude local contrast enhancement to obtain an enhanced image; performing hierarchical feature extraction to identify edge contours and key control points to form a multi-dimensional feature set; and establishing a dynamic reference coordinate system based on the feature set to obtain a part space attitude matrix. And the attitude deviation is compensated through Z-axis offset and rotation coupling error analysis. Posture adjustment is decomposed into a plurality of sub-stages, an alignment track is optimized by adopting a variable speed planning strategy, and accurate alignment of the parts is achieved. The problems that multi-view visual information fusion is insufficient, a special recognition algorithm for geometrical characteristics of the industrial parts is lacked, and Z-axis offset and rotation coupling error compensation is inaccurate in the posture adjustment process are solved, and the precision and stability of alignment of the industrial parts are improved.

The invention relates to the technical field of building information modeling, in particular to a building drawing geometric feature extraction and visual modeling method and system, and the method comprises the steps: carrying out the self-adaptive noise reduction, contrast enhancement and line refinement processing of an original building drawing image, and carrying out the automatic layer separation based on colors and line types; linear geometric features and specific symbol geometric features in the drawing image are extracted, and a geometric feature set is constructed; component instantiation, attribute assignment and topological relation reasoning are carried out by using a predefined building component semantic rule base, and a building component semantic network is generated; and mapping the semantic network to a parameterized three-dimensional modeling engine, calling an IFC standard three-dimensional template, performing parameter driving and automatic assembly, generating a three-dimensional building model with semantic information and a spatial structure, and performing visual output. According to the method, efficient and standardized conversion from a two-dimensional building drawing to a three-dimensional building model can be realized, and the method has the remarkable advantages of processing complex drawings and high-precision modeling.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention discloses a liquid medicine foreign matter detection method and system based on machine vision, and the method comprises the steps: obtaining multiband spectral data of a liquid medicine sample, and carrying out the normalization processing to generate a liquid medicine background standard spectral feature template; comparing the refractive index spectral characteristics of the template and the foreign matter-containing liquid medicine, and recording the surface roughness scattering mode and polarized light reflection angle change data of a foreign matter area; calculating a spectrum peak displacement quantized value according to the polarized light data, and extracting foreign matter edge spectrum gradient information in combination with a threshold value; calculating a transparency spectral attenuation coefficient based on the edge gradient information, and obtaining spectral contrast enhancement factors of different foreign matters in combination with the spectral response characteristic data; and if the enhancement factor does not reach the standard, adjusting the spectrum correction parameter and recalculating, fusing the scattering mode and the multi-angle incident spectrum response result to carry out consistency verification, and obtaining a final identification result containing the foreign matter material type and the danger level. The method can improve the drug detection accuracy and guarantee the drug quality safety.

The invention relates to a small-sample contrast enhancement fine tuning method and system based on a large language model, which are used for identifying named entities in recruitment texts. The method comprises the steps of performing cleaning and format conversion on an original recruitment text, and generating an input sample conforming to a natural language instruction format; under the condition that the labeled samples are insufficient, positive and negative sample pairs are constructed to enhance the recognition capability of the model on entity categories and boundaries; carrying out low-rank parameter updating on the pre-trained large language model by adopting a LoRA fine tuning technology, and reducing computing resource consumption in combination with 4-bit quantitative training; in a pre-training large language model reasoning process, through a multi-dimensional joint confidence evaluation mechanism, confidence of four dimensions of entity levels, lengths, types and contexts is synthesized, and low-confidence identification results are filtered after dynamic weighted normalization processing. The method is suitable for recruitment recommendation, talent matching and other downstream tasks, and has the advantages of high recognition accuracy, low training cost, high system robustness and the like.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention provides a compression condensing unit state monitoring method based on anomaly detection and a storage medium, and relates to the technical field of industrial equipment operation state monitoring. The method comprises the specific steps that multi-source time sequence data is collected to construct a compression condensing unit state data set, a thermal disturbance matrix is generated based on difference and thermosensitive weight weighting, and a sound and vibration superposition disturbance mapping value is formed through a frequency band interval and a sound and vibration sensitive factor; constructing a collaborative variation tensor by using an asymmetric disturbance difference score, a residual range response value and a collaborative perception factor, and generating a multi-polarity contrast enhancement value in combination with a time modulation period, a disturbance suppression factor, an adaptive projection weight and a direction enhancement item; on this basis, a folding manifold value and a structure partition memory kernel anomaly score are formed and mapped into an anomaly probability; the compression condensing unit anomaly detection model is trained through weighted cross entropy loss, the anomaly detection accuracy and reliability are improved, and anomaly detection of the compression condensing unit is achieved.

The invention provides an adaptive contrast enhancement video fusion algorithm for a complex logistics scene, and the algorithm comprises the steps: extracting environment features from an initial video data set, analyzing the inter-frame illumination change and object movement speed through employing a convolutional neural network, determining that the current scene is a peak period or a low-illumination environment, and obtaining environment change perception parameters; extracting a key frame from the second video data set, separating a foreground object and a background by adopting an image segmentation technology based on deep learning, and performing detail retention enhancement on the foreground object to obtain a third video data set; real-time verification is carried out on the final video output, if the processing delay exceeds a preset threshold value, the resource distribution proportion is adjusted through a feedback mechanism, mode switching and enhancement processing are executed again, and the updated video output is obtained.

The invention relates to the technical field of image processing, and discloses a defect detection method and device for a wafer chip. The method comprises the following steps: preprocessing an original wafer image, wherein the preprocessing comprises at least one of the following items: contrast enhancement, noise suppression and smoothing, sharpening enhancement and normalization processing; dividing the preprocessed wafer image into a plurality of chip areas, and extracting a feature vector of each chip area by using a convolutional neural network to form an initial node feature matrix; based on the spatial position relationship between the chips, constructing a spatial adjacency graph of the wafer; and inputting the initial node feature matrix and the spatial adjacency graph into a defect detection model, and outputting a wafer-level defect detection result. According to the method, comprehensive modeling of defects in spatial distribution and associated feature levels can be realized, and the accuracy and robustness of detection are improved.

The invention belongs to the technical field of image enhancement and recognition, and particularly relates to a radar image intelligent enhancement and recognition method and system based on multi-model fusion, and the method comprises the steps: carrying out the adaptive suppression of speckle noise of an original radar image; feature point detection is carried out, robust transformation matrix estimation and adaptive contrast enhancement are carried out, and a corrected and enhanced image is output; utilizing the generative adversarial network and multi-loss function collaborative constraint to obtain a texture reconstruction image; establishing an image-semantic double-flow network architecture, performing cross-modal attention fusion to obtain a fusion feature map, and outputting a target recognition result; performing time phase division on the texture reconstruction image, judging a change type, and outputting a change detection result; and outputting a processing report including the enhanced image, the target list and change analysis. According to the method, noise suppression, correction enhancement, texture reconstruction, target recognition and change detection are integrated, the defect of fragmentation processing in the traditional technology is overcome, and the overall processing performance and the actual application adaptability are improved.

The invention provides a multi-scale pyramid weighted fusion underwater image enhancement method based on double prior, and the method comprises the steps: obtaining a degraded underwater image, and carrying out the global and local cooperation body color calibration of the degraded underwater image; decomposing the color correction image into a base layer, a detail layer and a noise layer by adopting a variational decomposition algorithm; performing spectral prior and transmissivity loss constraint on the base layer image to obtain a defogged image; fusing the detail layer image and the noise layer image to obtain a filtered image; performing enhancement processing on the filtered image by adopting a nonlinear mapping and contrast enhancement strategy to obtain an enhanced image; performing multi-level feature integration and reconstruction on the defogged image and the enhanced image by adopting a multi-scale pyramid adaptive weighted fusion method to obtain an underwater image with natural color and high visual definition; according to the method, the traditional image processing and variational optimization thought is combined to effectively correct the color deviation of the degraded underwater image, the image contrast and the detail definition are improved, and the visualization effect of the underwater image is improved.

The invention relates to the technical field of image data processing, in particular to an image adaptive optimization processing method and system for laser printing output, and the method comprises the steps: carrying out the multi-scale feature analysis and fusion of an input original scanning image, and obtaining a fused feature distribution mapping matrix; performing adaptive contrast enhancement based on the fused feature distribution mapping matrix to obtain an enhanced contrast image; performing edge feature extraction on the enhanced contrast image by using an improved multi-direction edge detection algorithm to obtain an edge feature image with an enhanced edge; performing adaptive local texture analysis and enhancement on the edge feature image to obtain a texture enhanced image; and carrying out printing adaptability optimization based on the texture enhanced image to obtain a final optimized output image. According to the technical scheme, the quality of the image printed and output by the laser printing equipment is comprehensively and remarkably improved from multiple image processing dimensions.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.

The invention relates to an interventional therapy target accurate positioning method and system based on an ultrasonic image. According to the method, a binary mask is generated by extracting an acoustic shadow area in an ultrasonic image, contrast enhancement parameters of different areas are dynamically adjusted based on the binary mask, and target spot edge details are enhanced while shadow area noise is suppressed; a shadow mask attention mechanism is combined to guide a neural network to accurately segment a target point probability graph, a segmentation boundary is evolved and optimized by integrating a level set constrained by a shadow region shape, and local smooth compensation is performed on registration deviation of a three-dimensional reconstruction model and a preoperative image by using a shadow perception deformation field correction strategy; and finally generating a puncture path for avoiding the dangerous structure. The problems of fuzzy target boundary, segmentation error accumulation and deformation compensation misalignment of a traditional method under acoustic shadow interference are effectively solved, and the pertinence of image enhancement, the accuracy of target positioning and the safety of an intervention path are remarkably improved.

The invention relates to the technical field of focus labeling, in particular to a medical image focus labeling method and system based on deep learning, and the method comprises the steps: collecting a multi-modal medical image, and carrying out the noise reduction and contrast enhancement preprocessing to obtain a medical enhanced image; segmenting a focus area through a deep convolutional network and generating a focus labeling mask image; and performing post-processing optimization on the mask image, inputting an optimization result into the sub-pixel level annotation generation model, and outputting a high-precision focus annotation coordinate sequence. According to the method, image quality and focus visibility are improved through multi-modal preprocessing, accurate segmentation is realized by using a deep convolutional network, traditional pixel-level precision limitation is broken through in combination with post-processing and a sub-pixel-level model, the problems of noise interference, contrast difference and boundary blur are effectively solved, and focus labeling fineness and spatial positioning precision are remarkably improved.

The invention provides an adaptive weight distribution chromosome stripe contrast enhancement method and system, and the method comprises the steps: firstly improving the significance of a chromosome stripe structure and an edge contour in a chromosome gray image through an edge enhancement convolution operator; carrying out chromosome monomer target detection and instance segmentation on the metaphase chromosome based on an instance segmentation network of deep learning to obtain a chromosome monomer ROI region; then, extracting a structure tensor response of the ROI region of the chromosome monomer, designing an adaptive enhancement weight of a histogram merging interval based on a normalized difference between a response value and global statistical information, and performing reconstruction and interpolation on a histogram of the image of the ROI region of the chromosome monomer; and finally, adaptively enhancing the contrast of the chromosome stripe through iterative histogram matching. According to the method, the original image texture structure is kept, and meanwhile, the definition and the distinguishability of chromosome stripes are effectively and adaptively improved.

The invention discloses an asphalt mixture component segmentation method, device and equipment, and relates to the technical field of asphalt pavement construction control, and the method comprises the steps: obtaining an initial two-dimensional image of an asphalt mixture section, and carrying out the preprocessing of the initial two-dimensional scanning image, and the preprocessing comprises the contrast enhancement and light field correction. Component division is carried out on the preprocessed image through a double-threshold segmentation method, morphological post-processing is carried out on the initial segmentation image, and each separated component segmentation image is obtained; and calculating the difference value between the image area ratio and the actual volume ratio of each component segmentation image, and calculating the relative error between the image screening grading and the actual screening grading of the aggregate components. When the difference value or the relative error exceeds the preset threshold range, the image processing parameters are adjusted, the segmentation operation is executed again, the target segmented image is obtained, the difference value and the relative error of the target segmented image are both within the preset threshold range, and the problem that the segmentation precision of the asphalt mixture components is insufficient is solved.

A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and / or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and / or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.

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 invention discloses a foam space distribution measurement and calculation method for high-strength recycled aggregate concrete, relates to the technical field of building material detection, and aims to solve the problem of inaccurate foam distribution measurement and calculation. According to the method, a three-dimensional image acquisition and foam space distribution feature extraction process based on CT scanning is constructed, high-precision reconstruction of the foam structure in the high-strength recycled aggregate concrete is realized, that is, a standardized image is generated through noise filtering, gray level normalization and contrast enhancement, boundary detection, gray level aliasing feature modeling and a multi-feature judgment rule are combined, and the high-precision reconstruction of the foam structure in the high-strength recycled aggregate concrete is realized. Generating a two-dimensional foam label graph by adopting self-adaptive multi-threshold segmentation and connected domain marking, constructing a three-dimensional foam voxel model according to interlayer spacing stacking, calculating parameters such as total volume proportion, distribution density and interlayer aggregation trend, forming a spatial distribution feature set, and inputting a performance evaluation model in combination with a three-dimensional structure graph, so as to evaluate the performance of the three-dimensional foam voxel model. And outputting prediction indexes such as air tightness and the like, thereby realizing automatic and standardized measurement and reliable evaluation.

The invention discloses a fan blade defect detection method based on direction constraint edge extraction and texture discrimination, and the method comprises the steps: image enhancement processing: carrying out the brightness and contrast enhancement processing of an input original fan blade image, and obtaining an enhanced image; fan blade positioning: analyzing the main direction of the image by using a structure tensor, and positioning a fan blade area in combination with a direction constraint edge extraction method; and defect detection and extraction: performing morphological operation, connected domain analysis and texture fusion discrimination on the suspected region in the positioning result, and extracting a final defect region. The method is suitable for automatic identification of surface defects such as cracks and corrosion in an offshore wind power inspection image; the method has the advantages of high robustness, low false detection rate and adaptability to complex sea and sky backgrounds.

The invention relates to the technical field of tennis recognition, in particular to a tennis recognition model based on adaptive illumination variation, which obtains an image brightness histogram by inputting an image, recognizes a region with uneven illumination distribution, and executes local contrast enhancement and global modeling, thereby effectively enhancing details of a target region, and improving the recognition accuracy. And the detectability of the tennis ball under weak light and complex light is improved. In combination with multi-scale feature extraction and classification identification, robust detection of tennis balls in different sizes and motion states is realized, and the method is suitable for multi-angle and light source interference scenes. And meanwhile, trajectory prediction and restoration are performed on detection results in continuous image frames by adopting a DeepSORT algorithm, so that the space-time continuity of target tracking is guaranteed, and target loss caused by detection interruption is avoided. The whole scheme integrates image enhancement, detection and tracking mechanisms, improves the accuracy and stability of tennis recognition under a dynamic illumination condition, and is suitable for a complex tennis outdoor competition environment.

The invention discloses a multi-mode ultrasonic microscopic image contrast enhancement method fused with expert priori knowledge, and the contrast, definition and detection reliability of an ultrasonic microscopic image are improved. The method comprises the following steps: a multi-modal feature coding module based on a CLIP framework extracts feature representations of an ultrasonic microscopic image and an expert cue word, and maps the feature representations to a unified semantic space through a cross-modal alignment mechanism; designing a semantic guidance prompt module, constructing semantic elements which highlight detection demand guidance, and combining an image-quality description sample to carry out few-sample fine tuning so as to enhance the response capability of the model to specific semantics; an image enhancement and reconstruction module is designed, a coding-decoding structure and a cross-layer feature connection mechanism are adopted, contrast enhancement, detail reconstruction and noise suppression are realized under the guidance of a CLIP semantic vector, self-learning correction is carried out based on a standard grooving plate to improve contrast performance, and optimization training is carried out in combination with structural similarity loss and semantic consistency loss.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention discloses an automatic instrument reading method and system based on embedded machine vision. The method comprises the following steps: deploying an embedded image acquisition unit with wide dynamic imaging capability; performing multi-mode preprocessing of white balance correction, contrast enhancement and noise suppression on the image; self-adaptively identifying the instrument type and positioning a key area by using a lightweight convolutional neural network; hough transform and sub-pixel edge detection are adopted for a pointer type instrument to achieve high-precision angle analysis, and optical character recognition is completed for a digital instrument through a bidirectional long-short-term memory network; and finally, fusing multi-frame results through Kalman filtering, evaluating credibility, and outputting stable readings with timestamps. According to the technical scheme, high-robustness, high-precision and low-delay automatic reading can be realized at the embedded end, and the operation stability and the long-term adaptability of the system in a weak network environment are remarkably improved.

The invention discloses a long-distance binocular camera calibration optimization method based on multistage feature enhancement, and the method comprises the steps: carrying out the processing of a condition that a long-distance calibration plate has a highlight region and is fuzzy in angular points, employing a bilateral filter to suppress the reflection of a highlight mirror surface, reducing the reflection of light, and maintaining the definition of an image; the checkerboard texture is enhanced by adopting a CLAHE algorithm in combination with blocking processing and a contrast gain threshold value; a Sobel operator and a Harris corner response function are fused, gradient direction distribution characteristics of a pixel neighborhood are extracted through the Sobel operator, weighted fusion is carried out on the gradient direction distribution characteristics and the Harris corner response function, and the response intensity and specificity of a corner area are remarkably enhanced. A three-level image preprocessing framework including reflection suppression, contrast enhancement and corner enhancement is constructed, the problem of feature extraction of a traditional method under a complex illumination condition is solved, the corner area detection capability is enhanced, the problems of specular reflection noise, low contrast and corner blur are effectively solved, and the calibration precision is improved.

The invention discloses an image quality optimization method and system based on multi-scale feature decoupling and dynamic fusion, and the method comprises the steps: carrying out the multi-scale decomposition of an input degraded image, and extracting the feature components of illumination-color, texture-noise and edge-structure; illumination normalization and color fidelity enhancement are realized through illumination estimation and color space transformation; the base layer and the detail layer are separated through edge preserving filtering, and self-adaptive contrast enhancement and noise suppression are carried out on the detail layer; extracting edge information by using a multi-directional gradient operator, and strengthening significant structural features through nonlinear mapping; constructing a lightweight weight learning network, and generating a spatial self-adaptive dynamic fusion weight map according to the multi-scale features; executing progressive three-level fusion according to the dynamic weight, and reconstructing to obtain a high-quality image; according to the method, the image is decomposed into feature components with different physical meanings, targeted optimization and adaptive fusion are carried out, and more accurate and robust image quality improvement is realized.