233 results about "Image contrast" patented technology

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 artificial intelligence and electronic manufacturing detection, and discloses an ultra-thin printed circuit board defect detection method and system based on AI vision. The objective of the invention is to solve the technical problems of poor imaging quality, high missed detection and false detection rate, weak generalization ability and difficulty in consideration of efficiency and precision caused by buckling deformation, surface reflection and tiny and diverse defects of an ultrathin printed circuit board in the existing detection technology. The method comprises the following steps: performing multi-angle image acquisition through a composite illumination module and a high-resolution linear array camera; geometric correction is carried out on the image by using an SIFT algorithm and affine transformation, and the image contrast is enhanced by using a CLAHE algorithm; generating a defect candidate region through the lightweight full convolutional network; and carrying out accurate classification and identification on the candidate region by using a deep residual network. According to the technical scheme, high-precision, high-efficiency and low-false-alarm detection of the tiny defects of the ultrathin printed circuit board can be achieved, and the automation level and adaptability of detection are remarkably improved.

The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on a microwave frequency crystal resonator, and the method comprises the steps: forming a microwave probe through a microwave frequency crystal resonator array, placing a to-be-detected material below the microwave probe, and setting a scanning area and scanning step lengths in two directions on the surface of the material; controlling the probe to perform point-by-point scanning according to step length, and collecting a plurality of reflection coefficients of each sampling point to form an echo signal matrix; performing singular value decomposition on the matrix, applying an adjustable coefficient to a first singular value and a second singular value, and adaptively selecting an optimal coefficient according to imaging quality to reconstruct an echo signal; performing two-dimensional Fourier transform on the reconstructed matrix, constructing a spatial filter in combination with probe spacing and defect depth to perform phase compensation, performing two-dimensional Fourier transform to obtain a defect image, and performing threshold segmentation on the defect image to obtain a binary image only containing defects; and analyzing the connected regions to obtain the gravity center position of each defect and the defect area determined by the pixel number and the single pixel area. The problems of serious clutter interference, insufficient defect imaging contrast ratio, difficulty in quantitative evaluation of defect size and the like in the existing microwave detection can be solved.

The application discloses a method for rapidly and nondestructively detecting the quality of silicon carbide crystals, which comprises the following steps: placing the silicon carbide crystals on a computer tomography (CT) carrier table, and rapidly and nondestructively detecting the typical defects in the silicon carbide crystals by means of computer tomography under specific parameters, image contrast and the identification of the topographic features, so as to directly obtain the defect topography, density distribution and the evolution of the defects with the growth process of the measured ingot at different sections, and significantly improve the visualization degree of the defects in the silicon carbide crystals.

The invention relates to the technical field of bar code identification, and discloses a rapid image matching incomplete bar code accurate identification method, which comprises the following steps: carrying out image contrast enhancement processing based on bar code candidate area response on an incomplete bar code image; positioning a bar code area of the incomplete bar code image after contrast enhancement, and detecting and marking an incomplete area in the bar code area; carrying out inference and reconstruction processing on the incomplete area based on a bar code standard coding rule and a statistical redundancy mode; and carrying out identification decoding and redundancy check on the reconstructed bar code image. According to the method, through a multi-step processing mode of image contrast enhancement, incomplete area positioning and labeling, reconstruction based on coding rules and redundancy modes and identification verification, rapid and accurate identification of the incomplete bar code is realized, and under the condition that partial information of the bar code is missing, fuzzy and interfered, the bar code structure can still be effectively recovered, and the identification accuracy of the bar code is improved. And the accuracy, the stability and the robustness of bar code identification are obviously improved.

The invention relates to a distribution line unmanned aerial vehicle inspection image fusion method and system and a medium, and the method comprises the steps: carrying out the aerial inspection of a distribution line through a high-resolution visible light camera and an infrared thermal imaging camera carried by an unmanned aerial vehicle, and obtaining the image data of a key part; performing denoising processing on the acquired multi-modal image to reduce interference caused by environmental factors and sensor noise, and then enhancing the image contrast and improving the definition of image details through a histogram equalization method to obtain denoised and enhanced intermediate preprocessing data; according to the method, a power distribution line unmanned aerial vehicle image fusion model composed of an encoder and a decoder is constructed, image fusion is performed on source images of different modalities after denoising and enhancement, so that data advantages of different modalities are fully utilized, richer result images are generated, more comprehensive line state information can be provided, and the reliability of the power distribution line unmanned aerial vehicle image fusion is improved. More accurate state evaluation and fault diagnosis are realized, the inspection efficiency and accuracy are improved, the cost is reduced, and the safety is improved.

The invention discloses an intelligent enhancement method for a road image under a low-illumination condition, and belongs to the technical field of computer vision and video processing, and the method comprises the following steps: S1, calculating an actual illumination value of a current road scene; s2, adaptively adjusting the exposure time; s3, targeted noise reduction; s4, color temperature correction; s5, enhancing the image contrast; s6, performing strong light suppression to obtain a final current road scene image; under extremely low illumination, the image signal-to-noise ratio is increased by more than 40%, the color rendition degree is larger than or equal to 90%, clear full-color images can be output without external light supplementation, meanwhile, the strong light inhibition rate of vehicle headlights and the like is larger than or equal to 95%, the overexposure area is reduced to be lower than 5% of the original area, the algorithm processing delay is smaller than or equal to 40 ms, the real-time monitoring requirement is met, and the method is suitable for highway night scenes.

The invention belongs to the technical field of image contrast, particularly relates to a water conservancy safety monitoring system based on dynamic vision, and aims to solve the problems that in an existing water conservancy safety monitoring process, the image quality and the recognition accuracy are remarkably reduced under complex and dynamically changing illumination and environment conditions, and the image quality is poor. In order to solve the problems of unstable monitoring performance and insufficient reliability caused by the lack of adaptive optimization and multi-source information deep fusion capability, the invention provides the following scheme: the system comprises a data acquisition module, the data acquisition module is connected with an illumination evaluation module, the illumination evaluation module is connected with a scene classification module, and the scene classification module is connected with a data processing module. According to the invention, through a dynamic vision technology, adaptive image enhancement and multi-source data fusion of a water conservancy project under complex illumination and environment conditions are realized, the image quality and defect identification accuracy of all-weather monitoring are significantly improved, and the environmental adaptability and reliability of the system are enhanced.

The invention discloses an image fusion method based on a double-branch feature decoupling auto-encoder, and belongs to the field of computer vision and image processing. According to the method, for the problems of modal pollution and structural distortion in infrared and visible light image fusion, structural semantic information and high-frequency texture details of a source image are extracted respectively by constructing a content feature coding module and a detail feature coding module, and feature decoupling is achieved. And performing deep fusion on the decoupled features by using an adaptive feature weighting mechanism, and reconstructing a fused image with infrared target saliency and visible light detail definition through a shared decoder. According to the method, an end-to-end two-stage training strategy is adopted for optimization, complex prior or post-processing is not needed, the effects of improving the fused image contrast, edge preservation and target recognition performance are remarkable, and the method is suitable for the fields of weak light monitoring, intelligent perception, unmanned system navigation and the like.

The invention relates to the technical field of computer vision, and discloses a computer vision-assisted dental restoration adaptation system, which comprises an image acquisition module, an image processing module, an adaptation evaluation module, a central control module and a feedback optimization unit, high-resolution image data, including visible light images, three-dimensional scanning data and infrared images, of the interior of the oral cavity and a restoration body are obtained through multi-source imaging equipment of the image acquisition module, the form, texture and temperature distribution characteristics of the oral cavity environment are captured, and the image contrast and the alignment precision are enhanced in combination with the illumination control unit and the positioning calibration unit. Data integrity and detail visibility are ensured, and a reliable basis is provided for subsequent image processing; the image processing module performs preprocessing, feature extraction and segmentation on the acquired image data, so that the accuracy and efficiency of adaptive state analysis are improved.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.

The invention belongs to the technical field of X-ray imaging, and particularly relates to a grid structure and method for filtering scattered X-rays, the grid structure comprises a scintillator array and a grid partition plate arranged between scintillator units, and the grid partition plate is made of a high-density material. The core is that the grid ratio (H / L) of the grid is greater than or equal to 8: 1 and less than or equal to 20: 1, and the grid density is greater than or equal to 80 line pairs / cm and less than or equal to 150 line pairs / cm. Through the design of high grating ratio and high density, space filtering is carried out by utilizing a geometric channel, scattered X-rays in disordered directions are effectively blocked, and meanwhile, original rays are allowed to pass through. According to the method, energy filtering is indirectly realized, and low-energy scattering photons are preferentially absorbed. The method can significantly suppress image fog noise, improves image contrast and signal-to-noise ratio, and can be widely applied to various X-ray detection systems.

The invention relates to the technical field of image processing, in particular to a gland structure irregularity quantitative analysis method and system based on a gastroscope image, and the method comprises the steps: carrying out the color correction and image contrast enhancement of an obtained original gastroscope image, and constructing a corresponding image pyramid; inputting the images in the image pyramid into an improved deep learning model for learning by adopting a multi-channel fusion input mode, and obtaining a gland binary mask after morphological processing; based on three dimensions of morphology, structural arrangement and complexity, quantitative features of a single gland structure are extracted from the gland binary mask; the extracted multi-dimensional quantitative features are trained through a gradient lifting decision tree model, an irregularity index is output, feature importance analysis is carried out, and the gland structure in the gastroscope image can be accurately recognized and quantized.

The invention relates to the technical field of medical image processing, in particular to a multi-time-point neural image comparative analysis method and system.The method comprises the following steps that a first three-dimensional neural image matrix and a second three-dimensional neural image matrix are obtained, rigid transformation matrix registration is calculated, and a third three-dimensional neural image matrix is generated; constructing a three-dimensional elastic grid model and adjusting node coordinates based on a mutual information criterion; generating a three-dimensional displacement vector field; constructing a Jacobian matrix and calculating determinant values; executing natural logarithmic transformation to generate a microscopic volume change rate map; and calculating a logarithm Jacobian mean value and determining a volume change state. According to the method, pose deviation is eliminated by calculating a rigid transformation matrix, a vector field reflecting fine displacement is constructed by utilizing a mutual information criterion, voxel-level quantitative analysis is realized in combination with a Jacobian matrix and logarithmic transformation, and minimal lesion recognition precision and diagnosis efficiency are improved in cooperation with a standard brain anatomy template.

The present application relates to the technical field of image segmentation, and provides a lung image segmentation method, which comprises the following steps: S1: inputting a lung CT dicom sequence and converting the original dicom sequence into a jpg format image as an original lung image; S2: enhancing the original lung image through an image enhancement Lap-CLAHE algorithm to obtain an enhanced image; and S3: inputting the enhanced image into a U-shaped symmetrical network segmentation model 3D-SDUnet composed of an encoder, a decoder, a DPA double-path attention and a skip connection, and outputting a final segmentation result of the lung image. According to the technical scheme, the lung image is first enhanced, the detail information is improved, and the good image contrast is maintained, so that high-quality input data is provided for subsequent model segmentation. The segmentation model is optimized, the skip connection composed of a dense Swin Transformer block (DATB) and the DPA attention module are added, the information connection between the encoder and the decoder is strengthened, the segmentation precision is improved, and a complete lung model is obtained.

The present application relates to a kind of solar filament fine structure enhancement method based on line Gauss filter, belong to image enhancement field.To overcome the current H-alpha image in solar filament fiber because of low contrast, leading to artificial marking difficult, the problem that the fiber attribute information is not accurate enough is extracted, the present application proposes a kind of image enhancement method with line Gauss convolution as core.Firstly, H-alpha image is normalized to preliminary stretch image contrast, while suppressing noise using Laplace-Gauss operator to enhance edge, then using line Gauss filter to enhance the contrast of solar filament fiber.In post-processing stage, using the adaptive histogram equalization of restriction contrast and top hat, bottom hat transformation method to improve the uneven problem of line Gauss filter enhancement effect.Finally, line Gauss filter is used again to further improve the contrast of solar filament fiber.After the enhancement of the method, the filamentary structure of solar filament fiber is clear, which makes it possible to objectively and accurately measure the filamentary structure attribute information of solar filament.

The invention discloses a display height adaptive adjustment method and system based on user posture recognition, and the method comprises the steps: processing a user posture image through a lightweight MTCNN model, dynamically adjusting the image contrast based on a Retinex theory, and obtaining an enhanced user posture image; detecting eye pixel coordinates and shoulder key point coordinates in the enhanced user posture image by using a lightweight HRNet model, and converting the eye pixel coordinates into three-dimensional space coordinates with the center of a display as an original point in combination with depth map auxiliary distance data; acquiring a comfortable visual angle established by initial calibration of the user, calculating a sitting posture compensation amount based on the shoulder key point coordinates, and calculating a target height according to the sitting posture compensation amount; and performing Kalman filtering on the three-dimensional space coordinates, predicting an attitude change trend through a lightweight LSTM network, and generating an adaptive adjustment instruction according to the attitude change trend and the target height. And the height self-adaptive adjustment efficiency and accuracy of the display are improved.

The application relates to the field of semiconductor optoelectronics, in particular to a self-powered up-conversion imaging array and a preparation method thereof. The technical key points are as follows: array pixel units are arranged on an insulating substrate and are mutually isolated; each unit contains a central up-conversion detection subunit and a peripheral photovoltaic power supply subunit; the photovoltaic unit is formed by connecting micro photovoltaic cells in series, and the anode and the cathode are connected with the cathode and the anode of the central up-conversion detection subunit respectively, so as to form an independent closed loop series driving circuit in the pixel; the central up-conversion detection subunit is used for receiving incident near-infrared light signals and converting the signals into visible light signals; the peripheral photovoltaic power supply subunit is used for absorbing incident light and converting the light energy into electric energy to provide a driving voltage and a driving current; the peripheral photovoltaic power supply subunit and the central up-conversion detection subunit meet the current matching condition; the effects of self-power supply at the pixel level, complete elimination of electrical crosstalk between pixels, improvement of imaging contrast and resolution, improvement of photon utilization rate and device fault tolerance are achieved.

The invention relates to the field of photoelectric management, in particular to an intelligent photoelectric management system and method based on big data, and the system comprises a target detection module, an image processing module, a mechanical tracking module, a disturbance compensation module and a visual processing module. The image processing module is used for suppressing a sub-peak value and carrying out boundary truncation, the mechanical tracking module is used for continuously tracking a target, the disturbance compensation module is used for compensating disturbance of a target point, and the visual processing module is used for outputting a non-delay motion equation. The performance of the photoelectric tracking system in executing a tracking task is improved, the mechanical disturbance effect is effectively inhibited, high-precision tracking control over a target object is achieved, the self-adaptive capacity of the photoelectric tracking system is improved, and the stability of target tracking and the accuracy of motion data analysis are ensured.

The invention discloses a strain clamp X-ray image enhancement and defect identification method and system, and belongs to the technical field of image processing. The method comprises the following steps: acquiring an internal image of a wire clamp through X-rays, and performing feature reconstruction and defect modeling in a frequency space and a potential space; a multi-scale frequency encoder is adopted to extract detail features of a tension clamp image, a frequency domain structure selection module is combined to enhance defect edge response, and finally robust identification of an abnormal region is realized in a potential diffusion process. Therefore, good defect detection precision and boundary definition can be kept under the complex conditions of strong background noise interference, low image contrast and the like.

The invention provides an image contrast enhancement method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an initial image through a polarization camera, and enabling pixels in the initial image to be divided into high-gray-scale pixels and medium-low-gray-scale pixels; determining an alternative pixel value of the high-gray-scale pixel based on the light intensity information under the polarization angle of the polarization camera; determining a first gray scale range of the high gray scale pixels and a second gray scale range of the middle and low gray scale pixels based on the replacement pixel values of the high gray scale pixels; and determining a target pixel value based on the replacement pixel value, the medium and low gray scale pixels, the first gray scale range and the second gray scale range, and performing enhancement processing on the initial image through the target pixel value to obtain a target image. According to the technical scheme provided by the embodiment of the invention, the effects of improving the contrast of the image and further improving the quality of the image can be achieved.

The present disclosure relates to glare mitigation using image contrast analysis for autonomous systems and applications. In various examples, a contrast value corresponding to pixels of one or more images generated using one or more sensors of a vehicle can be computed to detect and identify objects that trigger glare mitigation operations. A pixel luminance value is determined and used to compute a contrast value based on comparing the pixel luminance value to a reference luminance value based on a set of pixels and corresponding luminance values. A contrast threshold can be applied to the computed contrast value to identify glare in the image data, thereby triggering a glare mitigation operation so that the vehicle can modify a configuration of one or more illumination sources to reduce the glare experienced by occupants and / or sensors of the vehicle.

The invention provides an X-ray flat panel detector and a preparation method of the X-ray flat panel detector. The X-ray flat panel detector comprises a substrate, a readout circuit layer located on the substrate, an insulating grid layer located on the side, away from the substrate, of the readout circuit layer, a first charge transmission layer located on the side, away from the substrate, of the readout circuit layer, and a perovskite material layer located on the side, away from the substrate, of the first charge transmission layer. The second charge transfer layer is located on one side, away from the substrate, of the perovskite material layer; and the top electrode layer is located on one side, away from the substrate, of the second charge transfer layer. The readout circuit comprises a plurality of pixel units. The insulating grid layer comprises a plurality of meshes, and the orthographic projection of each pixel unit on the substrate is located in the orthographic projection of one mesh on the substrate. The first charge transport layer is filled in the meshes of the insulating grid layer. And the perovskite material layer is filled in the meshes of the insulating grid layer. The X-ray flat panel detector provided by the invention has relatively high image resolution, image contrast and definition.

The application provides a kind of unmanned aerial vehicle control method based on real night image defogging, including the pre-processing of night fog image, the preliminary defogging of image after pre-processing is carried out using dark channel prior method, obtain a plurality of intermediate results.Based on image contrast, chroma, saliency and color contrast, a scoring model is established.Each intermediate result is input into the scoring model for scoring to obtain the final score of the kth intermediate result of the ith frame of night fog image.According to the final score, generate the binary weight of each intermediate result.According to the binary weight, all intermediate results are fused using a multi-scale fusion strategy.The fusion result is post-processed to obtain the final defogging image.The binary weight is used to selectively fuse the maximum information part of each pixel point of each intermediate result.The multi-scale fusion strategy is used to prevent the generation of artifacts while maintaining high contrast, improving the success rate and efficiency of unmanned aerial vehicle in executing night scene tasks.

The present application relates to X-ray image contrast enhancement technology, in particular to a kind of contrast enhancement method suitable for complex special-shaped workpiece X-ray image.The present application solves the problem that the existing X-ray image contrast enhancement method cannot give consideration to high contrast and low noise when applied to the X-ray image of complex special-shaped workpiece, is difficult to realize, and noise is serious.A kind of contrast enhancement method suitable for complex special-shaped workpiece X-ray image, the method is realized using the following steps: step one: collect the X-ray image of complex special-shaped workpiece;Step two: calculate the local deviation of X-ray image;Step three: calculate the enhanced contrast field;Step four: solve energy functional, obtain the iterative form of target image;Step five: the initial value of iteration is substituted into the iterative form of target image, and the target image is obtained by iteration.The present application is suitable for the X-ray image of complex special-shaped workpiece.

The invention discloses an image processing method and device, equipment, a storage medium and a computer program product, and belongs to the technical field of image processing. The method comprises the following steps: mapping a target pixel point in a target image to an original image to obtain a coordinate of a target mapping point; determining a plurality of reference pixel points from the original image based on the coordinates of the target mapping point; determining the edge intensity of the target pixel point based on the gray values of the plurality of reference pixel points; based on the edge intensity of the target pixel point and the distances between the multiple reference pixel points and the target mapping point, weights corresponding to the multiple reference pixel points are determined; and determining the pixel value of the target pixel point based on the pixel values of the plurality of reference pixel points and the weights corresponding to the plurality of reference pixel points. As the edge intensity can well distinguish the image edge and noise, the weight of each reference pixel point is adjusted by adding the edge intensity of the target pixel point, the image noise can be reduced, the image edge can be enhanced, and the image contrast can be improved.

The invention discloses a two-wheeled electric vehicle battery temperature change detection method and system based on machine vision. According to the method, an infrared image of a battery is divided into a basic image layer and a detail image layer for image enhancement processing; the method comprises the following steps: firstly, highlighting a target in a basic image layer and inhibiting a background by using significant features and non-significant features in the basic image layer so as to enhance the contrast ratio of the basic image layer; for the detail image layer, directly performing detail enhancement to obtain an enhanced basic image layer; then, fusing the two to obtain an enhanced infrared image, and performing image recognition to obtain a current temperature detection result of the battery; and finally, comparing the current temperature with the historical temperature to obtain a battery temperature change result. Therefore, the method effectively optimizes the detail area of the image while improving the image contrast, thereby improving the enhancement effect of the infrared image, and further improving the accuracy of battery temperature identification.

[Problem] A display device and a head-up display device are provided by which the image contrast can be increased more appropriately by adapting to the shape of a display image. [Solution] A display device comprises a lighting device for emitting illumination light, a liquid crystal display panel with an image display area 18b for displaying an image and a non-display area 18c in which no image is displayed, wherein the liquid crystal display panel receives the illumination light and emits display light, and a monochrome liquid crystal panel 12, which is arranged between the lighting device and the liquid crystal display panel and sets an area corresponding to the non-display area 18c to a black display state Sb and an area corresponding to the image display area 18b to a white display state Sw.

This invention provides a video image contrast enhancement method for warehouse monitoring scenarios, relating to the fields of image processing and video surveillance technology. It aims to solve problems such as insufficient contrast, unclear details in dark areas, amplified noise, and distorted edge structures in warehouse monitoring videos. The method preprocesses the video frames by performing grayscale normalization, constructing a variational energy function that includes a relative error fidelity term, an auxiliary variable consistency constraint term, a sparsity constraint term, and an adaptive regularization term. It then employs an iterative update method, alternating between the enhanced image and auxiliary variables, to achieve image contrast enhancement, noise suppression, and edge preservation. Finally, the enhanced video frames are reconstructed based on the original temporal information to obtain an enhanced video stream. This invention can improve the clarity and recognizability of video images in complex warehouse monitoring environments, enhancing the reliability of subsequent intelligent monitoring and analysis.

Embodiments disclosed herein include a waveguide combiner and method of forming a waveguide combiner. The waveguide combiner includes a substrate comprising a modified region, a plurality of optical devices disposed on the substrate, and a film layer disposed over the substrate. The modified region is treated to form a discoloration gradient. The waveguide combiner is modified using by treating the waveguide combiner to form a substrate modified region in the substrate to form a discoloration gradient.