53 results about "Color balance" patented technology

The invention provides a high-precision urban live-action three-dimensional model generation method and system and a storage medium. The method comprises the following steps: arranging photo control points in a target area and performing measurement; acquiring inclined image data of the target area by using the unmanned aerial vehicle; acquiring original point cloud data of the target area by using a laser scanner; performing aerial triangulation calculation on the inclined image data in combination with ground control point data to obtain aerial triangulation encrypted data; registering the original point cloud data in combination with the ground control point data, and then preprocessing the original point cloud data to obtain laser point cloud data; carrying out fusion reconstruction on the aerial triangulation encryption data and the laser point cloud data to generate an initial three-dimensional model; performing fine processing on the initial three-dimensional model to generate a high-precision three-dimensional model; according to the method, water surface cavities can be automatically repaired and efficient and intelligent color homogenization can be realized when the live-action three-dimensional model is constructed, so that the geometric accuracy and visual quality of the model are effectively improved, and engineering-level application requirements are met.

The invention relates to the technical field of image monitoring, in particular to an intelligent visual monitoring method and system in an extremely cold environment, and the method comprises the following steps: based on an extremely cold monitoring environment, collecting environment temperature data through a temperature sensor, measuring the wind speed and the snowfall density, and obtaining low-temperature environment state data; according to the invention, environment state data is acquired in real time, a battery charging and discharging strategy is automatically adjusted to adapt to low-temperature influence, a heating element is automatically adjusted to prevent the equipment from frosting and freezing, normal operation of functions of the monitoring equipment is ensured, and light sensitivity and exposure parameters of an image sensor are adjusted, so that the monitoring efficiency is improved. The image quality is improved, the influence of an extremely cold environment on the image quality is eliminated, the monitored data is more accurate and reliable in vision, the compression ratio is dynamically adjusted, key image data is preferentially transmitted, and the efficiency and the real-time performance of data transmission are enhanced.

The invention provides a self-supervised multi-task underwater image restoration method based on an underwater imaging model, and the method comprises the following steps: carrying out the color cast correction of an underwater image collected by an underwater robot through an adaptive color balance algorithm, and obtaining an image after the color cast correction; obtaining the transmissivity of the image after the color cast correction through a transmissivity estimation network; enabling the image after color cast correction to pass through a background light estimation network to obtain background light; enabling the image after color cast correction to pass through a clear image reconstruction network to obtain a reconstructed clear image; and the background light and the transmissivity of the reconstructed clear image are synthesized through a Jaffe-McGammery underwater imaging model, and training is carried out in a self-supervision mode to obtain a final enhanced image. According to the method, an end-to-end self-supervised learning architecture is adopted, network training can be completed without large-scale annotation of a data set, the method has the advantages of low training cost and high generalization ability, and efficient image restoration can be realized in a complex and changeable underwater environment.

This invention relates to an underwater image enhancement method based on piecewise color balance and multi-scale enhancement fusion, comprising: calculating the average grayscale value of each color channel and selecting a reference channel; obtaining a corrected first channel and a corrected first channel; decomposing the V channel into an original base layer and three scale detail layers; obtaining an enhanced detail layer; and generating the final enhancement result. This invention designs a gain factor to compensate for information loss in other channels, reduces color loss in the color-balanced image by considering the attenuation characteristics of different color channels, thereby satisfying the gray world assumption; it helps improve transmission estimation to obtain better detail enhancement and color preservation; it can estimate transmission without calculating the dark channel, reducing halos and computational complexity; it embeds a detail pyramid in the multi-scale fusion to compensate for detail loss in underexposed or overexposed areas of the underwater image; and the contrast and detail restoration of this invention are achieved at different scales.

This invention discloses a color balancing method, apparatus, electronic device, and storage medium. The method includes: acquiring four video images captured by a surround-view camera; performing distortion correction on the four video images to obtain four target video images; wherein the four video images are ultra-wide-angle images of the target object in four directions (front, back, left, and right); performing inverse perspective transformation on the four target video images to generate four top-view images; and performing color balancing on any one top-view image based on curve fitting to obtain the target top-view. This invention achieves color balancing through curve fitting, solving the problems of poor adaptability and low robustness of existing methods, and improving the adaptability and robustness of color balancing methods.

This invention provides an image forming module and projector that can achieve a desired color balance while suppressing the complexity of control. [Solution] The image forming module of the present invention comprises a first light source that emits first light, a second light source that emits second light, a first illumination optical system that generates first illumination light from the first light, a second illumination optical system that generates second illumination light from the second light, a first light modulator that modulates the first illumination light, a second light modulator that modulates the second illumination light, a photosynthesis element, and a control device. The first illumination optical system has a first light guide element that homogenizes the first light and a first parallelizing element that parallelizes the first light. The second illumination optical system has a second light guide element that homogenizes the second light and a second parallelizing element that parallelizes the second light. The control device applies the same current value to the first light source and the second light source, and the light utilization efficiencies of the first illumination optical system and the second illumination optical system are different so that the synthesized light has a desired color balance.

The invention belongs to the technical field of image processing, and provides a low-illumination image enhancement method based on an HSV (hue, saturation and value) color space, which comprises homomorphic filtering preprocessing, HSV space mapping, pixel truncation linear stretching, gamma correction processing, contrast enhancement processing, saturation optimization, RGB (red, green and blue) space mapping and color balance processing. Through fusion of pixel truncation linear stretching, gamma correction processing and contrast enhancement processing, parts with extreme pixel value distribution are eliminated, the pixel values of the image in a target range are enhanced, and the brightness and contrast of the low-illumination image are improved; through saturation optimization, the naturalness of the image is kept while the color brightness is enhanced, and color distortion is avoided.

The application discloses a kind of underwater image restoration pre-training-fine-tuning method, system and medium based on semantic and diffusion double prior, specific method: the basic pre-training network of shallow / deep feature extraction and double task reconstruction module is constructed;Adopting multiple degradation mode blind degradation strategy constructs pre-training dataset, trains model to extract generalization visual feature;Through embedding adapter module, fine-tune model on UIEB dataset with gradual thaw strategy, learn underwater specific illumination attenuation, color deviation distribution and scattering effect, realize cross-domain knowledge transfer;For denoising and super-resolution task embedding semantic prior module, fuse the semantic mask generated by SAM to strengthen image structure understanding;For super-resolution task, introduce diffusion prior module, play the advantage of diffusion model in complex image generation task, provide fine noise modeling and detail reconstruction capability for underwater image restoration model.The application has the advantages of maintaining color balance, restoring image details real delicate and the like.

Various implementations provide unvarnished video based on adjusting camera parameters according to environmental modeling. Environmental characteristics may be determined based on modeling a physical environment from sensor data captured via one or more sensors. For example, this may involve determining ambient light source optical characteristics, ambient surface optical characteristics, 3D mapping of the environment, user behavior, prediction of optical characteristics of light entering the camera, and the like. The method may involve determining camera parameters of an image captured via an image sensor based on environmental characteristics. For example, the method may determine exposure, gain, tone mapping, color balance, noise reduction, sharpness enhancement. The method may determine camera parameters based on user information (e.g., user preferences, user activity, etc.). The method may involve providing a transparent video of the physical environment based on the determined camera parameters.

The application discloses a bubble detection, tracking and parameter quantification method and device based on an improved YOLOv8. The method comprises the following steps: collecting continuous frame visible light RGB bubble images; converting the RGB bubble images into LAB color space, performing adaptive enhancement on an L channel, performing color balance on A and B channels, and obtaining a pretreatment image after standardization and normalization processing; inputting the pretreatment image into an improved YOLOv8 bubble detection model, setting a multi-scale feature enhancement module after a P4 layer of the model, and adopting a bidirectional feature fusion structure and a decoupling detection head to output a bubble detection result; performing cross-frame association based on continuous frame detection results to obtain a bubble trajectory sequence; and calculating bubble aspect ratio and motion speed parameters according to a detection bounding box and the trajectory sequence. The application is suitable for bubble detection, tracking and parameter quantification analysis in a complex background.

The invention provides a defogging and destreaking advanced method and device for an optical remote sensing image, and provides an optimization processing strategy of firstly removing thin cloud and then removing stripes for the common problems of stripe noise and thin cloud shielding in the remote sensing image, and is matched with an improved algorithm. Designing a segmented adaptive window moment matching fringe removal method based on an automatic sliding window, automatically dividing regions through cloud detection, dynamically determining a gray segmentation threshold value and the size of the sliding window, and effectively eliminating non-periodic fringes; and on the other hand, a shallow U-Net structure thin cloud and mist removing network with multi-scale cloud and mist feature fusion is constructed, an on-chip color balance loss function is introduced, and image splicing chromatic aberration is inhibited. According to the method, the image definition and the visual consistency can be remarkably improved, the problem of cloud and mist residue caused by'firstly removing stripes' or the problem of noise amplification caused by'firstly removing cloud and mist 'is avoided, high-efficiency and high-quality remote sensing image restoration is realized while texture details are kept, and the method is suitable for automatic processing of large-size high-resolution remote sensing data.

Consistent with disclosed embodiments, systems and methods for adjusting color balance across multiple cameras. Embodiments of the present disclosure may include a color balance unit. The color balance unit may include at least one processor programmed to receive at least one white point candidate and a spatial distribution from a first camera among a plurality of cameras and at least one white point candidate and a spatial distribution from a second camera among the plurality of cameras. The at least one processor may be configured to compare the at least one white point candidate and the spatial distribution received from the first camera with the at least one white point candidate and the spatial distribution received from the second camera and determine, based on the comparing, a target color balance level for use by one or more of the plurality of cameras in adjusting a color balance setting. The at least one processor may be further programmed to distribute the target color balance level to the one or more of the plurality of cameras.

The invention discloses a self-adaptive underwater image enhancement method based on multi-domain fusion and color compensation, and belongs to the technical field of image processing, and the method comprises the following steps: S1, obtaining an underwater image, and carrying out the data preprocessing of the underwater image; s2, carrying out adaptive underwater defogging compensation processing on the preprocessed underwater image; and S3, performing color balance processing on the underwater image after the adaptive underwater defogging compensation processing to complete underwater image enhancement. According to the method, the defects of insufficient cross-environment generalization ability, inaccurate color recovery, large calculation burden and the like in the prior art are effectively solved, and the method has better underwater environment adaptability and real-time processing potential in combination with self-adaption, a physical principle-based module, selective filtering and a color optimization strategy; and a more accurate and efficient technical scheme is provided for fine perception of the underwater environment.

A sunglass lens for providing ocular bio-stimulation while also providing ocular sun protection, clear vision, and appropriate color vision for safe driving.

The application provides a film, a preparation method thereof and an eye protection device. The film comprises a metal oxide and a second metal, the content of the metal oxide in the film is greater than the content of the second metal in the film, the metal oxide contains a first metal, the first metal and the second metal are both transition elements, and the atomic numbers of the first metal and the second metal are different. The film comprising the metal oxide and the second metal is non-toxic and has good chemical stability, so the stability of the film is strong, and the film can meet the requirements of long service life and multi-environment application of a display product using the film. On the other hand, the film can effectively absorb most of harmful blue light with a wavelength range of 415nm-455nm, while retaining most of beneficial blue light of other wave bands, ensuring high transmittance requirements of other visible light remaining wave bands, maintaining color balance of transmitted light, and avoiding secondary damage of reflected blue light.

[Problem] To provide a light guide plate with good color balance. [Solution] This light guide plate comprises a light guide substrate and at least one light-deflecting part formed on the light guide substrate. The light-deflecting part has a light entry part that in-couples light having a prescribed viewing angle to the light guide plate, a light exit part that allows the light in-coupled by the light entry unit to exit the light guide plate, and a splitting deflection part disposed between the light entry part and the light exit part to split the light into at least a first light portion and a second light portion. The first light portion has a greater proportion of blue light and a smaller proportion of red light than the second light portion. The propagation angle of blue light at the center of the viewing angle of the first light portion is equal to or greater than the propagation angle of green light at the center of the viewing angle of the second light portion, or is equal to or greater than the propagation angle of red light at the center of the viewing angle of the second light portion.

Provided is a method for processing a video. The method for processing the video can segment an initial video into a plurality of video clips, and calculate color temperatures of the plurality of video clips. For each target video clip in the plurality of video clips, the method can determine a color temperature adjustment coefficient of the target video clip based on a color temperature of the target video clip and a color balance correction coefficient thereof, and adjust a color temperature of at least one video frame in the target video clip by using the color temperature adjustment coefficient.

An optical device includes a light emitting element and an optical combiner. The light-emitting element is configured to emit an invisible light. The optical combiner is arranged in a light path of the invisible light. The optical combiner includes a series of fluorescent bodies. The series of fluorescent bodies are configured to be excited by the invisible light to emit a visible light. Therefore, the light color perceived by a user can be adjusted and enhanced, so that color balance is achieved, and the visual quality of augmented reality experience is improved.

The application discloses a video quality diagnosis method based on standardized color conversion and multi-stage adaptive detection, and relates to the technical field of video quality diagnosis.The YUV original data of a video frame to be diagnosed is acquired, offset compensation and RGB and HSV color space conversion are completed, the saturation histogram of the HSV image is counted, the neutral color saturation threshold is adaptively determined through the cumulative distribution function, the neutral color candidate area is screened, and a diagnosis result is output according to the hue distribution.The application synchronously adopts three-stage grading detection, luminance dynamic threshold compensation, symmetrical hue mapping, sliding window dominant hue detection, color balance verification, area weighting and time domain consistency verification, effectively solves the problems of color conversion misalignment, poor threshold robustness and high false alarm rate of natural scenes in the prior art, greatly improves the precision and stability of video color cast diagnosis, and adapts to the automatic operation and maintenance requirements of large-scale distributed monitoring networks.

The invention belongs to the field of computer vision and digital image processing, and discloses an underwater image enhancement method based on asymmetric gray world and gradient distribution characteristics, which comprises the following steps of: firstly, performing color correction on R, G and B channels of a degraded underwater image through an asymmetric gray world algorithm; and then determining a detail gain coefficient according to the local gradient of the underwater image, and carrying out adaptive detail enhancement on the corrected three channels according to the detail gain coefficient. According to the method, firstly, the color balance of the underwater image is optimized by using the asymmetric gray world algorithm, and meanwhile, the detail part in the image is restored through adaptive detail enhancement, so that the color and definition of the underwater image are improved.

The invention discloses a non-linear correction method and system for radiation brightness of a remote sensing sequence image. The method comprises the following steps: S1, carrying out ground object classification on a remote sensing image to extract ground object pixels with the same name; s2, constructing a nonlinear model based on local consistency constraint so as to eliminate local radiation difference between the images; s3, establishing a global constraint for keeping the statistical characteristics of the whole image set unchanged so as to ensure the overall radiation fidelity of the image and avoid tone drift; and S4, solving an optimization model jointly formed by local and global constraints, obtaining correction coefficients of all the images, and completing correction. The method is suitable for automatic production of large-scale, multi-source and multi-temporal remote sensing image mosaic products, complex non-linear radiation differences can be effectively processed, the generated mosaic products are natural in visual effect and smooth in color transition, and excessive correction of radiation brightness and overall hue drift are successfully avoided while color balance is achieved.

A color balance calculation system includes an acquisition unit and a calculation unit that calculates a color balance. The calculation unit calculates the sum of areas of a first system color, the sum of areas of a second system color, and the sum of areas of an accent color on the basis of a base exposed area that is visible on a base surface, the color of the base surface, an object exposed area that is visible on each placed object, and the color of each placed object. The calculation unit calculates the ratio of the sum of the areas of the first system color to the sum of the areas of the second system color to the sum of the areas of the accent color as a color balance.

The invention provides an anti-blue-light lens containing perovskite quantum dots. The anti-blue-light lens at least comprises a base material and the perovskite quantum dots dispersed in the base material, the chemical formula of the perovskite quantum dot is CsPb (BrCl) 3: Sr, wherein Sr is strontium ions doped into perovskite crystal lattices; the emission peak of the perovskite quantum dot is located in the wave band range of 480-510 nm, the half-peak width is not larger than 25 nm, and harmful blue light in the wave band of 415-445 nm can be absorbed and subjected to light conversion; in addition, the perovskite quantum dots also generate a synergistic absorption effect on part of red light and green light in transmission light, so that three primary colors of red, green and blue are synchronously attenuated, thereby reducing lens yellow color cast caused by blue light reduction, and realizing consideration of blue light prevention performance and visual color balance.

The invention discloses a railway bridge intelligent inspection method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring image data of railway bridge inspection through a sensor carried by an unmanned aerial vehicle; carrying out disease image enhancement on the image data by adopting a fast automatic color equalization method; frame labeling is carried out on the enhanced image data, and a sample data set for railway bridge apparent disease recognition is constructed; a target detection algorithm based on deep learning is adopted to construct an intelligent recognition model of railway bridge apparent diseases; and analyzing the image data through the intelligent identification model, and automatically identifying and classifying railway bridge diseases. The system comprises a data acquisition module, a disease image enhancement module, a data set construction module, a model construction module and an analysis and identification module. According to the invention, the automation and intelligence level of bridge inspection can be effectively improved, the labor cost is reduced, the working safety is enhanced, and efficient, accurate and real-time bridge health detection is provided.

The invention provides a multi-spectral remote sensing image rapid color homogenizing method based on a low-resolution reference map, which comprises the following steps of: dividing a to-be-homogenized multi-spectral image into independent processing blocks in a block processing manner by taking the low-resolution reference map as reference data for color homogenizing; the color homogenizing reference of each processing block is obtained from the reference data of the surrounding block, a Poisson Blending color homogenizing algorithm is used, color transfer is carried out from the peripheral edge of each block to the center direction, the pixel size of the grid blocks is controlled, it is ensured that the center area of each block after color homogenizing keeps high consistency with a reference map, and the color homogenizing accuracy is improved. Uniform light and uniform color of all block processing results in an area and a reference map are realized on the whole. The key steps involved in the method are realized by adopting a mature algorithm, and the method is fully automatic and has relatively high stability and applicability.

This invention discloses a method for color balancing remote sensing images based on surface reflectance and surface type. The method first inputs the remote sensing image and its auxiliary parameters, performs image calibration, converts DN values ​​to apparent radiance, and then further converts them to apparent reflectance. Subsequently, based on aerosol parameters, atmospheric correction is performed using a radiative transfer model to obtain the surface reflectance. Next, orthorectification of the surface reflectance is performed based on RPC information, and combined with surface type data, surface type information within the image range is obtained through geometric location matching. Then, a reference image is selected, and the mean and variance of surface reflectance for each channel of both the reference image and the image to be sized are calculated for different surface types. Finally, the mean-variance method is used to color-balance the image to be sized, obtaining a color-balanced surface reflectance image. This invention can effectively eliminate color deviations between remote sensing images, improving the color consistency and ground feature realism of mosaic images.

The application discloses an image enhancement method and device, a storage medium and a computer program product. The method comprises the following steps: obtaining an original image, wherein the original image comprises a plurality of pixel points with a first pixel value; performing color balance processing on the first pixel value of each pixel point in the original image to obtain a first intermediate image, wherein the first intermediate image comprises a plurality of pixel points with a second pixel value, and the second pixel value is obtained by performing color balance processing on the first pixel value of the corresponding pixel point; performing contrast optimization on the second pixel value of each pixel point in the first intermediate image to obtain a second intermediate image, wherein the second intermediate image comprises a plurality of pixel points with a third pixel value; and performing gray scale adjustment on the third pixel value of each pixel point in the second intermediate image to obtain an enhanced target image. The application solves the technical problem that the visual quality and color restoration of an image after being enhanced by a related algorithm are poor.

The application provides a panoramic system chroma adjusting method, which comprises the following steps: acquiring images of each camera; determining overlapping areas of the images of each camera; selecting a first image as a reference image of a panoramic image and a second image as a to-be-stitched image in the overlapping areas; acquiring an initial homography matrix between the reference image and the to-be-stitched image; correcting the initial homography matrix to obtain an optimized homography matrix; stitching the reference image and the to-be-stitched image through a bilinear interpolation algorithm according to the optimized homography matrix to obtain a panoramic stitched image; and judging whether the color balance degree of the panoramic stitched image meets a preset value; if yes, panoramic image stitching is completed; otherwise, the color of the panoramic stitched image is adjusted. The application realizes color balance and uniformity of the panoramic stitched image and improves imaging quality.

The invention discloses an unmanned aerial vehicle visual navigation method based on deep learning, and belongs to the field of navigation, and the method comprises the steps: collecting image information, including a color image and a depth image, of a surrounding environment through a high-resolution visual sensor carried by an unmanned aerial vehicle; meanwhile, inertial measurement unit data of the unmanned aerial vehicle is collected for assisting attitude estimation and motion compensation; preprocessing the collected color image, including but not limited to denoising, contrast enhancement and color balance operations; according to the method, through multi-modal feature extraction and fusion, the advantages of a color image, a depth image and IMU data are fully utilized, the perception ability to the environment is improved, through constructing an adaptive mixed learning model, deep learning and reinforcement learning are combined, autonomous navigation decision of the unmanned aerial vehicle is realized, and the method is suitable for being popularized and applied. According to the invention, scene identification and segmentation are carried out on the color image by using the semantic segmentation network, the unmanned aerial vehicle can be helped to identify passable areas and obstacles, and the navigation safety is improved.

This invention relates to the field of image processing technology, and in particular to an underwater image enhancement method based on a color channel interactive attention mechanism. The method includes: separating the color channels of the original underwater image to obtain R, G, and B single-channel images; extracting sparse features for each channel using a sparse feature estimation module; achieving cross-channel feature interaction and information fusion using a channel interactive attention module; generating fusion parameters through an environment perception module to optimize the interactive feature fusion effect; reconstructing the fused features into corresponding channel images; and stitching them together to obtain the enhanced underwater image. This invention effectively improves the color balance, structural consistency, and detail preservation of underwater images, and is suitable for underwater vision applications such as underwater robots and ocean exploration.