4444 results about "Lightness" patented technology

The invention relates to the technical field of defect detection, in particular to a special equipment defect automatic identification method and system, and the method comprises the following steps: marking a continuous path based on an image gray scale gradient and adjacent differences, identifying the pixel connection intensity to obtain a contour image layer, extracting continuous pixels, analyzing gray scale and gradient features, and expanding a texture direction to generate a defect image block. And marking and connecting the small regions to obtain a closed layer, analyzing texture and edge differences, matching classification tags, and clustering and coding to generate a defect identification tag set. According to the method, boundary structure recognition is enhanced through gray gradient and direction continuity analysis, a texture aggregation area is expanded in combination with gray consistency and edge direction stability, brightness abrupt change points are removed, contour extraction precision is improved, micro crack continuity is recovered, and pseudo defects are eliminated; and multi-dimensional image attributes are fused to identify key region feature differences, so that the classification precision and the spatial mapping consistency are improved, and efficient and accurate defect identification and stable classification are realized.

The invention discloses a low-illumination image enhancement method based on a Retinex theory, and belongs to the field of low-illumination image enhancement. According to the method, on the basis of the Retinex theory, three aspects of innovations are developed: a double-branch decomposition network DecomNet is established, and precise decoupling of illumination and reflection components is realized; a diffusion model is introduced to denoise reflection components, and noise suppression and detail retention are both considered by means of a self-constraint consistency loss function; reLumenNet is constructed, an LIT module and a CBAM mechanism are fused, and illumination is regulated and controlled in a self-adaptive mode. By means of a series of innovative designs, the low-illumination image enhancement effect is greatly improved, and the problems of brightness distortion, noise interference, detail missing and the like are effectively solved. The method has high practical value in the scenes of night monitoring, automatic driving and the like, and a novel low-illumination image enhancement method is provided on the technical level.

The invention relates to the technical field of display control, in particular to a display self-monitoring method and system and a display, and the method comprises the following steps: collecting light intensity data, comparing and classifying ambient light as low light, strong light or reflected light with a preset standard, judging whether a real-time color temperature and a standard color temperature difference value exceed a threshold value, if yes, adjusting the color temperature and the brightness, and if not, adjusting the brightness; pixel RGB channel proportions are extracted frame by frame, a color gamut distribution diagram is generated, restoration instruction time features are recorded, the color temperature adjustment trend is counted, and a backlight gradient compensation mode is generated. In the invention, the light intensity around the display is monitored in real time, the light intensity is compared with the preset illumination condition, and the ambient light condition is automatically classified, so that the display can adjust the color temperature and the brightness according to different illumination environments, and the backlight drive of the display is adjusted in time by analyzing the coverage areas of the red, green and blue channels in the display frame frame by frame; the adaptive capacity and the long-term stability of the display under variable illumination conditions are improved, and more optimized visual experience is provided for users.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention relates to the technical field of image recognition processing, in particular to a black and odorous water body recognition method and system based on image recognition processing. The method comprises the following steps: based on black and odorous water body image data, adjusting a pixel value range through sampling, comparing color channel distribution, extracting gray value distribution characteristics, analyzing partition brightness values, and generating a basic characteristic data set; according to the method, the pixel value range is adjusted, the gray value distribution characteristics are extracted, the partition brightness value analysis is carried out, the characteristics of the water body pollution area are refined, the texture gradient variation and the gray value distribution are analyzed, the pollution area boundary is divided in combination with accumulation analysis and edge strength, and the pollution types are classified in combination with geometric parameters, gray values and color channel changes. The diffusion direction and radius are analyzed through vectorization expansion, the coverage range is constructed in combination with density and path information, the accumulated pollution value is measured and calculated, the pollution identification accuracy and the dynamic tracking capability are enhanced, and the evaluation systematicness is comprehensively improved.

The invention relates to the technical field of image recognition, in particular to an image monitoring system for traditional village heritage risk assessment, and the system comprises a heritage image collection module which is used for obtaining an image data stream of a target traditional village building surface, carrying out the geometric correction of original heritage image data, carrying out the image brightness equalization processing, and obtaining an image data stream of the target traditional village building surface; and establishing a calibrated image set. According to the method, image distortion and local overexposure caused by shooting angle difference or uneven illumination on the surface of a traditional village building are eliminated through geometric correction and brightness equalization processing, and the input data quality of subsequent feature analysis is improved. The consistency degree of crack textures in a neighborhood range is quantified based on gray gradient direction field data, the gray contrast of continuous crack edges is enhanced in combination with a dynamic threshold adjustment mechanism, non-structural texture interference is inhibited, and separability of micro cracks and background materials is enhanced.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention provides an infrared and visible light image perception enhancement fusion method and system based on deep learning, and the method employs a multi-stage training strategy, and comprises the steps: carrying out the single-mode reconstruction training of an encoder and a decoder, so as to guarantee the feature extraction and reconstruction capability; based on a pre-training encoder and a pre-training decoder, performing multi-scale decomposition by applying discrete wavelet transform, and performing enhancement and fusion on the basic features and the detail features respectively; and designing an illumination perception module and an illumination consistency loss function to optimize the brightness and contrast of the fused image. The method effectively improves detail retention, brightness uniformity and color expression of the image under the low-light condition, and is particularly suitable for application scenes such as night high-perception requirements and the like.

The invention relates to the technical field of light source control, and discloses a self-adaptive regulation and control method and device for wafer detection light source brightness, and the method comprises the steps: carrying out the preliminary irradiation of a wafer through a preset annular light source, collecting initial image data, carrying out the digital simulation according to the initial detection data, and generating a wafer detection feedback model; analyzing a light source irradiation demand according to the detection feedback model, generating a gradient execution scheme, sequentially configuring light source parameters according to the gradient scheme, carrying out multiple rounds of detection on a wafer, collecting data, feeding detection data back to the model, carrying out parameter adjustment, carrying out expanded light source detection demand analysis on the adjusted model, and generating an expanded detection scheme. According to the method, by automatically adjusting the brightness and configuration of the light source, efficient and accurate wafer surface defect detection is achieved, the detection precision and efficiency are improved, the method adapts to the detection requirements of different wafers, and the problem that in the prior art, due to the fact that the light source used for detection is not comprehensive enough, the defect is not detected is solved.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses a complex material-oriented global full-frequency illumination neural rendering method and system, and belongs to the technical field of computer graphics, and the method comprises the steps: querying and extracting a primary frequency feature and a secondary frequency feature in a neural feature field according to the primary geometric buffer and the reflection geometric buffer of a rendering pixel; performing weighted combination on the primary frequency features and the secondary frequency features through a double-frequency fusion module to form full-frequency fusion features; inputting the full-frequency fusion feature and the initial geometric buffer into a decoder to obtain a pixel radiation brightness value; and optimizing the network by adopting a double-stage training strategy of low-frequency data and multi-frequency data, and then using the optimized network for neural drawing of global full-frequency illumination. The method can improve the accuracy and stability of global illumination simulation in a complex dynamic scene, and is suitable for modeling a multi-frequency global illumination effect in a dynamic three-dimensional scene.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention provides a computer visual defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the steps that according to collected multi-angle images, the overall area of an electric control board is partitioned in combination with illumination conditions and visual angle information; performing image partitioning processing to generate a plurality of image sub-regions, and extracting texture features, brightness distribution features and geometric edge features in each image sub-region; rare defect feature enhancement processing is executed, multi-scale repeated superposition is carried out on low-frequency abnormal textures, and directional extension is carried out on edge fractures; performing difference comparison with the corresponding normal area combination features, and performing normalization correction in combination with the illumination condition and the visual angle information; multi-angle reproducibility analysis is executed, when the same suspected defect is detected at different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; according to the invention, the accuracy of defect detection is improved.

The invention discloses a fishery underwater image enhancement method based on a multi-branch joint correction network, and the method comprises the steps: carrying out the denoising of an original underwater image through a minimum image segmentation noise separator, and obtaining a brightness channel and a color channel; secondly, decoupling a brightness channel to obtain intrinsic characteristic information and brightness information, and designing a brightness correction network to eliminate brightness degradation of the image; and performing multi-scale feature extraction on the corrected brightness channel, and performing wavelet transform on the brightness channel to a frequency domain to obtain an enhanced brightness channel. And carrying out difference operation on the two brightness channels before and after brightness correction, and then carrying out binarization processing to obtain an artificial light source illumination area mask. And finally, carrying out artificial light source illumination area mask-guided color restoration on the color channel, outputting an enhanced color channel, fusing the enhanced color channel with an enhanced brightness channel, and outputting an enhancement result. According to the method, detail reduction and color calibration of the underwater image can be effectively realized.

The invention discloses a heat shrink tube detection method based on image recognition, particularly relates to the technical field of electric power material defect detection, and is used for solving the problems of defect contour distortion, positioning deviation and insufficient classification precision caused by optical ghost interference in heat shrink tube detection. A mapping relation of light refraction paths is established based on brightness gradient difference and refractive index difference, theoretical ghost interference fringes are generated by dynamically adjusting optical path difference of an interferometer, and abnormal areas deviating from theoretical distribution are screened; meanwhile, light source incident angle optimization and defect displacement calculation are combined, residual ghost interference is eliminated, finally, accurate classification and positioning of defects are achieved through multi-dimensional matching of geometric parameters and historical defect data, complex algorithm dependence is reduced while the detection precision is improved, and the real-time detection requirement of an industrial production line is met.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention discloses a stainless steel tube surface defect detection method and system. The method comprises the following steps: S1, collecting stainless steel tube scanning images under dark field and bright field illumination; s2, splicing the line scanning images into dark field and bright field cylindrical expanded images, and executing brightness equalization and reflection suppression processing; s3, carrying out pixel-level fusion on the dark field and bright field cylindrical expansion images according to a set weight; s4, inputting the fusion cylinder expansion image into a YOLO network trunk of an integrated Swin Transform module, and extracting a multi-scale feature map; s5, performing feature fusion and bounding box prediction, and outputting a bounding box, confidence and a category label of the defect; s6, performing semantic segmentation, gray segmentation and weighted fusion in the detection frame area to generate a final defect mask; and S7, calculating the area, length, width and centroid coordinates of the defect, and converting the actual size and the spatial position. The stainless steel tube surface defect recognition accuracy and stability are improved.

The invention belongs to the technical field of digital image processing, and particularly discloses a low-illumination image enhancement method for simulating a foggy day degradation mechanism, and the method comprises the following steps: generating a virtual haze image; estimating atmospheric light searched by combining decreasing cutting with a genetic algorithm; coarse transmissivity calculation based on dark channel prior; refining the transmissivity graph; carrying out defogging and low-illumination enhancement on the virtual haze image; and brightness and contrast self-adaptive compensation. According to the method, the precision and robustness of atmospheric light value estimation are improved, a transmissivity graph which better conforms to a scene can be obtained, and the image restoration effect is improved; the method has remarkable advantages in the aspects of reducing artifacts, enhancing image details, denoising, keeping structural information and restoring details.

The invention discloses a dark light enhancement method under the view angle of an unmanned aerial vehicle, and relates to the technical field of image processing and enhancement, and the method comprises the steps: collecting a continuous frame dark light image sequence of a target when the unmanned aerial vehicle flies, carrying out the preprocessing operation including denoising and normalization, and forming a dark light image set; and estimating the motion between adjacent frames by using an image recognition algorithm. According to the invention, through dynamic range compression and detail enhancement processing, the image definition and visibility in a dark light environment are improved, the brightness difference of the image is balanced by adopting a dynamic range compression algorithm, local overexposure or underexposure is avoided, the image can keep a good visual effect under different illumination conditions, and the image quality is improved. And the detail enhancement processing highlights texture and edge information in the image through multi-scale gradient fusion and adaptive sharpening, and improves the detail definition in dark light, so that the unmanned aerial vehicle can recognize a target more clearly when executing a task at night or in a low-light environment, and the task execution efficiency and accuracy are improved.

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses a circuit board surface coating quality detection method and device, and the method comprises the steps: obtaining an image of a coating surface, and removing noise interference; brightness gradient information is extracted, and when the brightness gradient information exceeds a preset brightness threshold value, it is judged that defects exist, and a potential defect area is obtained; a potential defect area is separated from the background, image brightness distribution is mapped into plating depth data, and surface topography parameters are calculated; the defect areas are classified, edge optical characteristics are extracted from coating edge areas in the image, the coating thickness difference is calculated, the thickness difference and the edge optical characteristics are fused to obtain fusion characteristics, and an image is generated through an algorithm; and calculating the brightness dispersion degree of the plating layer edge and the local thickness difference of each edge region, carrying out weighted average on the corresponding pixel gray value to obtain a fused comprehensive defect region image, and calculating to obtain the comprehensive quality score of the plating layer. The method can accurately detect the quality of the circuit board.

Embodiments of the present application disclose an ambient light compensation method and apparatus, an electronic device, a storage medium, and a program product. The method comprises: capturing an ambient image in response to the distance between an object and a distance sensor being less than a preset threshold; determining an original light color and a color cast value of current ambient light on the basis of color information in the ambient image, wherein the color cast value represents a color distribution state of the original light color in a color gamut; on the basis of the original light color and a preset target light color of target ambient light, determining a compensation light color corresponding to the current ambient light; determining light brightness adjustment parameters on the basis of the compensation light color and the color cast value; and on the basis of the light brightness adjustment parameters, using the compensation light color to perform light compensation on the current ambient light.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention discloses a backlight source partition dimming control method, and belongs to the field of data security, and the control method comprises the following specific steps: I, analyzing the data features of an input image, recognizing a highlight motion region, and predicting and optimizing a current backlight partition dimming strategy; iI, adjusting a backlight area in real time according to the brightness, contrast and color change of the input content, and constructing a multi-level light diffusion coupling model after partition adjustment; the backlight partition can be adaptively adjusted according to the image content, the local contrast ratio and energy efficiency are improved, more uniform light distribution is achieved, the problems of halo and uneven brightness are reduced, the dimming precision is improved, the service life of a light source is prolonged, the light overflow effect of the partition edge is effectively reduced, and the picture layering sense is improved; the subjective image quality experience is effectively improved, the light efficiency utilization rate is improved, meanwhile, image quality and power consumption optimization can be considered, brightness adjustment better conforms to human eye visual characteristics, visual fatigue is reduced, and it is ensured that the optimal image quality is maintained under the lowest power consumption.

The invention provides a low-light large dynamic image enhancement method based on dynamic weight and pyramid fusion. The low-light large dynamic image enhancement method is suitable for complex illumination imaging in security monitoring. According to the method, the global weight and the detail weight of an image are obtained according to the global average brightness, the local brightness and the gradient of an image sequence, and the total weight is obtained through weighting. And the halo phenomenon easily occurring in the fusion process is reduced through guide filtering. And combining Gaussian pyramid and ratio pyramid decomposition and weighting to obtain a final fusion image. According to the method, the problems of detail loss and limited dynamic range in the low-light scene are effectively solved. Experiments show that the method is superior to the prior art in information entropy, average gradient, edge strength, spatial frequency and other key indexes. The technology has an all-time adaptive capability, is suitable for the fields of intelligent transportation, urban security and protection, low-illumination monitoring and the like, and has a wide market prospect.

A visual defect detection method, device, medium and product relate to the field of visual detection, and the method comprises the following steps: firstly, recording brightness changes of pixel points in different illumination directions by constructing an illumination response matrix, and establishing a complete mapping of surface reflection characteristics; secondly, performing joint calculation by using the light source direction matrix and the illumination response matrix to obtain normal vector data and reflectivity data, and representing spatial geometric features and material optical features of the surface respectively; thirdly, calculating a gradient feature map, a curvature feature map and a deformation feature map based on the normal vector data, generating a reflectivity map by using the reflectivity data, and forming a multi-dimensional feature representation system; and finally, in a defect detection stage, a strategy of combining gradient feature pre-screening with multi-feature verification is adopted, and final judgment is carried out by calculating a comprehensive defect feature value, so that the technical problem of insufficient micro defect detection precision of a visual detection method in the prior art is solved.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention relates to the field of illumination control, and discloses a regional illumination control method and system, and the method comprises the steps: obtaining the equipment state information, illumination intensity and personnel image information of each sub-region of a target region; the sub-regions are determined based on the building layout and function purpose division of the target region; identifying a personnel activity type and a personnel position based on the personnel image data; obtaining a static illumination demand of each sub-region of the target region; the static illumination demand comprises a brightness value and a color temperature; inputting the personnel position, the personnel activity type, the illumination intensity and the static illumination demand of each sub-region into an illumination demand prediction model; the illumination demand prediction model outputs an expected illumination demand of each sub-region; and generating a lighting control strategy for an optimization target based on the expected lighting demand of each sub-region in combination with the lighting comfort and the service life of the lighting equipment. The method has the following effect that the light requirements of people in different activity scenes are met.