258 results about "Gamma correction" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of fan blade health monitoring, in particular to a vibration-illumination double dynamic compensation fan blade cracking early warning method and system. The method comprises the following steps: triggering and capturing a video key frame at a key angle through a rotation angle sensor, and carrying out image coarse alignment by utilizing SIFT feature matching; a corrected motion vector field is generated by combining physical parameters of the rotating speed and the angular acceleration, vibration time sequence compensation is carried out, and illumination time sequence compensation is completed through Gamma correction by taking a clear frame as a reference; an improved DeepSORT algorithm is adopted to associate crack tracks with crack mask contour similarity, evolution characteristics are extracted and fused with SCADA working condition data, an expansion rate model is established, and the remaining life is predicted; early warning grades are divided according to the service life and the expansion rate, four-dimensional operation and maintenance data are fused to output differential operation and maintenance strategies, and model parameters are optimized through feedback data. According to the invention, the accuracy and early warning reliability of crack detection in a complex environment are effectively improved.

The invention discloses a fluorescent microscopic image intelligent enhancement method and device and a readable storage medium, and belongs to the field of medical image processing. The method aims at solving the problems that multi-channel fusion of fluorescence images is difficult, pseudo-color mapping is not accurate, RGB processing is not uniform, and parameters depend on experience. The method comprises the following steps: acquiring a multi-channel image and separating; performing pseudo-color mapping and synthesis based on wavelength physical characteristics to obtain a synthesized image; obtaining R, G and B three-channel target histogram features of the standard library; carrying out R, G and B three-channel independent histogram specification on the synthesized image; by utilizing a genetic algorithm, taking similarity with a target histogram as a target, and adaptively optimizing independent Gamma parameters of three channels R, G and B; and performing independent Gamma correction by using the optimal parameter to obtain a final enhanced image. According to the method, physical accurate mapping, standardized correction and parameter self-adaption are realized, and the image quality is improved.

The invention discloses an HDR video reconstruction method based on brightness alignment, and the method comprises the steps: carrying out the gamma correction of continuous frames of an input LDR video, generating an HDR image domain, and splicing the HDR image domain with an original frame to form an input tensor; constructing a brightness alignment network model, and generating brightness alignment features through a brightness attention module; generating detail features through a detail synthesis module; performing dynamic weighted fusion on the brightness alignment features and the detail features through an adaptive mixing layer, and performing up-sampling to obtain final alignment features; and finally, generating an HDR video frame by fusing the final alignment features. Through collaborative optimization of the brightness attention mechanism and the time domain alignment module, the problem of brightness inconsistency in a motion scene is effectively solved, and the HDR reconstruction quality is remarkably improved.

The present invention discloses an image defogging method based on multi-scale coding and decoding, comprising: S1, constructing an image dataset containing foggy images and standard fog-free images, and preprocessing the dataset; S2, constructing a multi-scale coding and decoding defogging network, and training the network using the pre-processed image dataset; S3, processing the image to be defogged using the trained multi-scale coding and decoding defogging network to obtain a defogged image. The present invention adopts a dual-branch architecture and performs multi-scale adaptive fusion of the features of the two branches, enabling the model to achieve excellent performance when using a real haze dataset of limited size; introduces brightness constraints into the combined loss function, giving positively correlated attention to regions of different concentrations, enabling the network to more effectively process non-uniform haze images; the present invention performs gamma correction on the training data of different datasets to reduce the brightness difference between the datasets, and then performs data fusion to effectively improve the defogging performance.

The invention relates to the technical field of machine vision detection, in particular to a video detection system for conveyor belt deviation and foreign matter accumulation, which comprises a video acquisition module, a self-adaptive illumination compensation module, a multi-scale feature fusion module, a space-time consistency analysis module and a decision output module. Wherein the video acquisition module is used for acquiring an original video stream of an operation area of a conveying belt; the self-adaptive illumination compensation module is used for generating an illumination equalization image through dynamic background separation and self-adaptive gamma correction; the multi-scale feature fusion module is used for extracting and generating a plurality of feature maps; the space-time consistency analysis module is used for outputting a deviation azimuth angle, a foreign matter coordinate and a confidence coefficient; and the decision output module is used for generating a deviation grade, a foreign matter type and an alarm instruction according to a preset threshold value. According to the invention, high-precision identification and classification judgment of the deviation of the conveying belt and the accumulation state of the foreign matters are realized through cooperation of multiple modules, and the intelligent level and the practical response capability of the detection system are improved.

The invention belongs to the technical field of image enhancement, and particularly discloses a low-illumination image enhancement method based on an information fusion strategy, and the method comprises the steps: carrying out the processing of an inputted low-illumination image through an image enhancement algorithm based on adaptive Gamma correction, and generating a first group of multi-exposure image sequences; processing the same input image through an improved quadratic function enhancement algorithm to generate a second group of multi-exposure image sequences; and performing multi-scale fusion on the first group of multi-exposure image sequences and the second group of multi-exposure image sequences, including distributing the weight of each image through a weight strategy, performing multi-scale decomposition on the images by using a Gaussian pyramid and a Laplacian pyramid, and reconstructing and generating a final enhanced image after layer-by-layer fusion. According to the method provided by the invention, the advantages of brightness enhancement and color optimization are combined, and an image with balanced brightness, rich details and natural colors can be generated in a low-illumination scene.

The invention discloses a gating image low-illumination enhancement method based on an improved RRDnet network, and the method comprises the steps: carrying out the processing of a collected low-illumination gating image through employing an RGRRDnet model, and obtaining an enhanced image. In RGRRDnet, firstly, an image is divided into a reflection component, a noise component and an illumination component by using a residual decomposition network; secondly, locally adjusting an illumination component by adopting a two-dimensional adaptive Gamma correction method, and improving the problem of uneven illumination of a gated image; meanwhile, the reflection component generated by the decomposition network and the reflection component obtained through calculation are fused through an adaptive weighted fusion method, and the influence of noise estimation errors is effectively reduced; and finally, introducing a detail enhancement module, and enhancing the edge and texture information of the image by using DEConv convolution operation. Experimental results show that the method is superior to a current mainstream low-illumination enhancement algorithm in the aspects of objective indexes and subjective vision on a gated image data set.

The invention relates to the technical field of liquid crystal display, and discloses a liquid crystal display module driving method. The method comprises the following steps: performing pixel time sequence analysis on a liquid crystal display module, and establishing a pixel addressing model; the liquid crystal display module is divided into a dynamic display area and a static display area according to the pixel addressing model, and a double-frequency partition scanning driving framework is obtained; submatrix division and submatrix importance level calculation are carried out on the liquid crystal display module based on a double-frequency partition scanning driving framework, and a differential refresh frequency distribution table is obtained; comparing the current frame with the previous frame of the liquid crystal display module according to the differential refresh frequency distribution table, and identifying a changed pixel region to obtain a differential update data packet; and transmitting the differential update data packet, adjusting a gamma correction parameter and a driving voltage parameter, and outputting a target driving control instruction. According to the invention, collaborative optimization of time domain and space domain of the liquid crystal display module is realized, and the driving display precision of the liquid crystal display module is improved.

The invention relates to the technical field of operator safety management, and discloses a non-contact physiological status rapid screening method and system and a medium, and the method comprises the steps: collecting an RGB three-channel space-time image sequence of a facial region of interest; carrying out illumination correction on the RGB three-channel space-time image sequence by adopting an adaptive gamma correction algorithm, carrying out end-to-end noise reduction processing on the illumination correction image sequence by utilizing a one-dimensional convolutional neural network auto-encoder noise reduction algorithm, and separating a remote photoelectric volume pulse wave tracing pulse signal; calculating physiological indexes of heart rate, heart rate variability and oxyhemoglobin saturation based on the remote photoplethysmography pulse signal; and performing grading judgment on the physiological indexes based on the physiological index grading standard, determining a final post suitability judgment grade in combination with the wooden barrel effect model, and generating a grading early warning signal. According to the method, accurate and rapid evaluation of the physiological state of the operator in a complex environment can be realized, the individual health degree can be comprehensively and objectively considered, and decision support is provided for reasonable distribution of posts and efficient configuration of personnel.

The invention relates to a point cloud boundary segmentation method and system for a tooth color three-dimensional digital model, and the method comprises the steps: carrying out the gamma correction of a tooth HSV color model, and obtaining a point cloud with the local contrast enhancement of a V channel of an original point cloud HSV color model, and a seed point as a boundary line; obtaining and calculating the similarity between each field point and the seed point according to the feature information of field points in a certain field range of the seed point, and taking the points of which the similarity is greater than a preset similarity threshold as boundary points of the boundary line; after performing quality evaluation on the boundary points of the boundary line, dynamically adjusting the weight of gamma correction until the boundary points of the boundary line under the optimal quality evaluation are obtained; and obtaining a closed boundary line according to the finally obtained boundary line points. The method fully reduces noise interference by fusing multiple features such as color, normal and curvature, effectively improves the accuracy and stability of boundary segmentation in combination with dynamic weight, reduces the cost of manual parameter adjustment, and is suitable for point cloud data under different illumination and geometric conditions.

The invention discloses an electric energy meter assembling standard intelligent identification system, which comprises an image acquisition module, an image preprocessing module, a wiring identification module and a standard comparison module, and is characterized in that the wiring identification module carries out NLM denoising and Gamma correction enhancement processing on a to-be-identified image, dynamically calculates a threshold value by using an Otsus method, accurately detects the position of a sleeve in an electric meter image, and carries out standard comparison on the to-be-identified image; and acquiring the coordinate of each casing, and identifying the casing fields by using PaddleOCR (Passive Optical Character Recognition). According to the method, through specific tests, wiring diagram shooting, threshold value dynamic calculation through an Otsus method, contrast enhancement through Gamma correction, NLM denoising, text detection, character recognition, geometric correction and other modes, OCR recognition is carried out on the image, algorithm and formula optimization of different degrees is carried out in the recognition process, the detection precision and efficiency are remarkably improved, and the method is suitable for popularization and application. And an effective solution is provided for automatic detection of the electric meter.

The invention provides a defect enhancement method and device based on image adaptive threshold segmentation. The method comprises the following steps: acquiring an original defect image of a defect in a non-marginal region; converting the original defect image into a single-channel grayscale image; extracting an edge region and a central region of the single-channel grayscale image according to a preset width and a preset proportion; calculating an edge pixel mean value according to the pixel value of the edge region, and calculating a central pixel mean value according to the pixel value of the central region; determining a defect type according to the edge pixel mean value and the center pixel mean value, and calculating a gray scale boundary value; constructing an enhancement coefficient array based on Gamma correction; performing defect enhancement on the single-channel grayscale image according to the grayscale boundary value and the enhancement coefficient array, and outputting an enhancement result image; the method can effectively improve the accuracy of defect enhancement.

The invention relates to the technical field of forestry detection, and discloses a wood internal defect nondestructive testing method and system based on a forestry CT image, and the method comprises the steps: obtaining the projection data of wood through a forestry CT scanning device, reconstructing the projection data through employing a maximum likelihood expectation iteration method, and generating a wood internal density distribution image; histogram equalization and Gamma correction are carried out on the wood internal density distribution image, the contrast of a defect area is enhanced, and a preprocessed image is obtained; inputting the preprocessed image into an improved MobileNetV3 model, generating a pixel-level mask graph of the defect, and marking the position and the range of the defect to obtain a defect area; classifying defect types on the basis of the segmented defect regions, calculating the area, the volume and the density attenuation rate of the defect regions, and outputting a defect severity degree evaluation result through an improved MobileNetV3 model; the detection efficiency is improved, and the wood does not need to be physically damaged.

The invention relates to an anchor hole recognition and positioning system and method, anchor protection equipment and a storage medium. The anchor hole recognition and positioning system comprises a visual sensor for collecting original image information within preset time, a laser radar for obtaining original point cloud information within the preset time and an image processor. The image processor comprises a first processing module and a second processing module; the first processing module processes the original image information based on an adaptive gamma correction algorithm and a defogging algorithm to obtain updated image information, and determines a two-dimensional target pixel area according to the updated image information and an anchor hole recognition model obtained by preset training; the second processing module processes the original point cloud information based on a pre-constructed first joint calibration relation between the visual sensor and the laser radar to obtain a three-dimensional target area; and then processing the three-dimensional target area to obtain an anchor hole position and an anchor hole center. The anchor hole identification precision can be improved.

Provided are an image processing device, a display device, and a control method whereby the luminance of an overlapping region in a projection image can be suppressed from decreasing non-linearly according to the position in the overlapping region by edge blending. This image processing device comprises an arithmetic circuit and a storage device, and transmits an output video signal to a display device. The arithmetic circuit performs gamma correction on a received input video signal using a first gamma characteristic and a second gamma characteristic, performs edge blending for adjusting the luminance of a predetermined region in an image according to the position in the predetermined region using the second gamma characteristic, and generates an output video signal. The second gamma characteristic is the inverse function of a third gamma characteristic of the display device. The third gamma characteristic has a gamma curve expressed by an exponential function. The predetermined region is a region in which the image displayed by the display device and another image displayed by another display device overlap.