532 results about "Color correction" patented technology

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

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention discloses an underwater image enhancement method based on an improved MobileNetV4 (MobileNetV4). The method comprises the steps of firstly constructing a degraded underwater image data set and performing normalization preprocessing; a generator with MobileNetV4 as a trunk is provided, wavelet denoising (WD), color correction (CC), a multi-query attention mechanism (MQA) and a LayerScale layer are integrated, and high-frequency noise reduction, color temperature adjustment and feature stabilization are achieved; in cooperation with a multi-scale discriminator (MSD), Huber adversarial loss, Smooth L1 similarity loss and dual-channel content perception loss optimization training are adopted. Compared with a traditional method, the model is smaller in parameter quantity and calculation quantity, but better performance is achieved, the UIQM index is improved by more than 15%, the requirements for light weight and enhanced quality are remarkably balanced, and the method has practical value in the fields of ocean exploration, biological monitoring and the like.

The invention relates to the technical field of image processing, in particular to an underwater crack segmentation-oriented color correction and texture sharpening double-branch enhancement system, which is characterized in that a data set is used for training, reasoning and analysis, and underwater crack images are stored in the data set; the system comprises an input unit used for receiving an underwater crack image; the color correction network is used for carrying out color correction on the underwater crack image and eliminating the problems of color deviation and low contrast of an underwater environment; the texture enhancement network is used for performing texture enhancement on the image after color correction; and the output unit is used for outputting the image enhanced by the texture enhancement network, and aims to recover the color and texture features of the underwater crack image through the color correction network and the texture enhancement network so as to enhance the perceptibility of the semantic segmentation model to the underwater crack.

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably improved.

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

The invention provides a multi-degradation type adaptive image enhancement method and device, and the method comprises the steps: obtaining a degraded image, extracting degradation features in the degraded image, and generating a degradation vector; dynamically adjusting a preset weight coefficient of the multi-scale feature extraction branch based on the degradation vector, and performing weighted fusion on the extracted multi-scale features according to the adjusted preset weight coefficient to obtain a fused feature; on the basis of the degradation vector and the fusion feature, generating a detail mask and a noise mask, and optimizing the fusion feature to obtain an optimized fusion feature; and mapping the optimized fusion feature into a preliminary enhanced image, performing color correction on the preliminary enhanced image based on color statistical information of the degraded image, and outputting a final enhanced image. According to the method and the device, through self-adaptive degradation perception and dynamic weight fusion, a single algorithm can adapt to various degradation coexistence scenes, and the limitation of traditional series processing is broken through.

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

The invention belongs to the technical field of deep learning, particularly relates to a two-stage blind image defogging method based on prior guide diffusion, and aims to solve the problem of distortion of a traditional decontamination method in a complex scene. Comprising the steps that a double-stage blind image defogging model is constructed, and the double-stage blind image defogging model comprises a first stage and a second stage; wherein in the first stage, physical modeling is carried out based on an improved atmospheric scattering model, the improved atmospheric scattering model is an enhanced atmospheric scattering model in which a light absorption coefficient is introduced, and a transmission image, a fogless reference image and atmospheric light parameters are output through the first stage; in the second stage, generation optimization is carried out based on a diffusion model, the transmission image, the fog-free reference image and the atmospheric light parameters output in the first stage are used as physical priori to be fused into the generation process of the diffusion model, and image defogging is carried out. In the second stage, self-adaptive difference fusion convolution is set, a fog domain multi-source fusion attention mechanism is set, and a pixel level and wavelet domain double-effect color correction strategy is adopted.

The invention discloses a target tracking method and device based on a rolling and pitching type holder, and the method comprises the steps: building a polynomial response model through adaptive color calibration, and achieving the dynamic color correction; an improved YOLOv8 model is combined with a bidirectional feature pyramid network to carry out multi-scale target detection, and template matching is assisted to improve a low-confidence-coefficient scene recognition rate; predicting the position and the speed of the target by using Kalman filtering to cope with a shielding condition; and high-precision smooth rotation of the holder is realized through quaternion attitude solution in combination with PID (Proportion Integration Differentiation) and FOC (Fiber Operating Control) double-loop cooperative control. The device adopts a rolling and pitching type holder structure and comprises a main controller, an angle sensor, a visual sensor and a magnetic encoder, the mechanical structure is simplified, the weight is reduced, and higher response speed and higher control precision are achieved. The target tracking accuracy, stability and anti-interference capability of the intelligent mobile platform in a complex environment are remarkably improved, and the method is suitable for various dynamic scenes such as aerial photography and industrial inspection.

The invention discloses an underwater image enhancement method and system based on double-domain collaboration. The method comprises the following steps: acquiring a degraded underwater image, preprocessing the degraded underwater image, extracting shallow layer features of the preprocessed degraded underwater image, performing frequency domain optimization on the shallow layer features through a frequency domain ocean resonator model, and acquiring frequency domain enhancement features; extracting space phase amplitude characteristics of the preprocessed degraded underwater image through a space domain ocean context aggregation convolution model; performing local enhancement dynamic adjustment on the space phase amplitude characteristics according to the frequency domain enhancement characteristics; image reconstruction is performed according to the adjusted spatial phase amplitude characteristics through a preset closed-loop cooperation mechanism of frequency domain global optimization and spatial domain local refining, and an enhanced underwater image is output, so that the enhancement intensity of the local region of the image can be dynamically adjusted on the basis of fusion of frequency domain guide information; therefore, coherence and consistency of a color correction strategy in space are ensured, and synchronous adjustment of space and local structures is realized.

The invention discloses an underwater target detection and image recognition method based on multi-scale feature fusion, belongs to the technical field of target detection, and solves the problem that in the prior art, in an underwater image preprocessing stage, the adaptive ability is generally lacked, and effective balance between background noise suppression and detail reservation is lacked, so that the detection robustness is insufficient. The method comprises the following steps: pre-constructing a target detection and recognition model based on ResNet-50 in combination with MobileNetV3, preprocessing underwater image data, performing multi-scale feature fusion detection on an image data set by the target detection and recognition model, and outputting a lightweight target recognition result; according to the method, dynamic estimation and compensation can be carried out on spectral attenuation and scattering effect, water body scattering ghosts can be removed, details and texture information of a target can be reserved in the color correction process, clear and real input is provided for subsequent detection, and on the premise that real-time performance is guaranteed, the detection accuracy is improved. And the accuracy, the recall rate and the recognition robustness of underwater target detection are greatly improved.

The invention discloses a color correction method, system and device based on cooperative flight of double unmanned aerial vehicles, a medium and a product, and relates to the technical field of agricultural detection. According to the method, a double-unmanned-aerial-vehicle cooperative flight mode is adopted, one unmanned aerial vehicle (namely the first unmanned aerial vehicle) flies at a high height for aerial photographing, the other unmanned aerial vehicle (namely the second unmanned aerial vehicle) carries a color card to synchronously fly at a low height, multiple farmland images containing the color card are obtained, a color correction matrix is constructed based on the RGB value of the color card in the farmland images and the actual RGB value of the color card, and the color correction matrix is obtained. Color correction under a dynamic illumination condition is realized, the problem of color distortion in a low-altitude remote sensing imaging process of the unmanned aerial vehicle is overcome, the reusability of cross-sensor data is improved, accurate acquisition of crop colors by the unmanned aerial vehicle in a field environment is realized, and reliable data support is provided for accurate crop management.

The invention discloses a glass curtain wall three-dimensional modeling method, device and equipment and a storage medium. The method comprises the steps that one or more image groups obtained by shooting a target glass curtain wall are acquired; matching the first visible light image and the first thermal infrared image of the same image group to obtain a second visible light image and a second thermal infrared image which are matched with each other; according to the temperature of each pixel in the second thermal infrared image, identifying the position of a glass panel pixel in the second thermal infrared image; according to the positions of the glass panel pixels in the second thermal infrared image, color correction is carried out on the glass panel pixels in the second visible light image, and a third visible light image is obtained; and performing three-dimensional modeling on the target glass curtain wall by using the one or more third visible light images. According to the embodiment of the invention, a real, clear and complete high-precision glass curtain wall three-dimensional model can be constructed.

The invention provides a digital pathological image high-color accurate splicing method and device based on feature point matching. The method comprises the following steps: obtaining a target splicing region in a target splicing image and a to-be-spliced region in a to-be-spliced image based on a scanning sequence of digital pathological images; performing affine transformation on the to-be-spliced region by using the affine transformation matrix to obtain an initial correction region; constructing a global color mapping function, and mapping the initial correction image by using the global color mapping function to obtain a color correction image; and splicing the color correction image and the target splicing image based on the scanning sequence of the pathological images to obtain a digital pathological image splicing result. According to the scheme, only the target splicing area and the to-be-spliced area are subjected to feature point matching, the cumulative distribution function of the effective feature points is calculated according to the RGB channels, and the global color mapping function is constructed and optimized, so that accurate color correction is realized, and the visual integrity and consistency of the spliced image are improved.

The invention relates to the technical field of insulation degradation detection, in particular to an insulation characteristic degradation detection method for an underwater wet-plugging electric connector, which is characterized in that a multi-angle appearance image of the connector is acquired through a pressure-resistant underwater camera device, and the image quality and the recognition accuracy are improved by adopting a defogging and color correction technology. And further extracting defect characteristics such as cracks, corrosion and aging on the surface of the connector through an image by virtue of a graph neural network model, realizing automatic identification and positioning of defect types, and then executing a local electrical test on the identified defect region to obtain electrical parameters such as insulation resistance, capacitance value and medium absorption ratio. Image recognition information and electrical test data are constructed into a feature matrix, a fusion evaluation model composed of a logistic regression model and a weighted scoring mechanism is input, an insulation degradation degree value is output, and a detection result is generated, so that the accuracy of insulation detection of the underwater wet-plugging electric connector is improved, and the detection time is effectively shortened.

The invention provides an end-to-end lightweight underwater target detection method and system, and belongs to the field of target detection, and the method comprises the steps: carrying out the frame sampling of an underwater video stream at a fixed interval, and obtaining an original image as a to-be-detected image; sending the original image into a differentiable brightness enhancement module and a lightweight texture enhancement module in parallel to generate a color correction image and a texture enhancement image in sequence, and inputting the original image, the color correction image and the texture enhancement image into a space correlation gating attention fusion module to obtain a fusion enhancement image; inputting the fusion enhancement graph into a backbone network formed by connecting a plurality of feature extraction layers in series and then connecting the feature extraction layers in series with a spatial pyramid pooling layer, and outputting a multi-scale feature graph; sending the multi-scale feature map into a path aggregation network to obtain a fused feature map with consistent semantics; and inputting the fusion feature map and the output of the spatial pyramid pooling layer into a multi-branch detection head, completing category, bounding box and confidence prediction, and outputting a final detection result through non-maximum suppression.

Disclosed is a surveillance camera capable of detecting a color of a vehicle using vehicle logo detection. This specification describes a method that, instead of processing an entire image of a vehicle, detects the vehicle's logo and expands a bounding box of the detected logo to analyze a dominant color within the expanded bounding box for higher accuracy. During a dominant color analysis process, color correction may be performed based on metadata, environmental factors, and a lighting condition. In addition, more accurate vehicle color detection may be achieved through color distance measurement. According to this specification, at least one of a surveillance camera, an autonomous vehicle, a user terminal, and a server may be associated with an Artificial Intelligence (AI) module, a robot, an Augmented Reality (AR) device, a Virtual Reality (VR) device, and a 5G service.

The invention relates to an underwater image quality improvement method based on Retinex heuristic color correction and saturation driving. The method comprises the following steps: obtaining an underwater image after color correction; extracting a brightness channel, and decomposing the brightness channel into a base layer and three detail layers; estimating the transmissivity of the underwater image after color correction; and obtaining an underwater image with enhanced contrast and details, namely an enhanced image. According to the method, segmented channel compensation based on the maximum mean value is performed on the basis of channel characteristic difference observation of an underwater scene, the difference can be adapted in a targeted manner, and accurate channel compensation is realized; the color brightness is improved; the overall visual experience of the image is improved, and the color cast problem of the underwater image is effectively corrected; the accuracy and efficiency of underwater image transmissivity estimation can be obviously improved; according to the method, the fog effect removal of the image base layer and the enhancement of the gradient information of the multi-level detail layer are synchronously realized by using the estimated transmissivity, and finally the high-quality underwater image is generated.

The invention belongs to the technical field of LED driving, and particularly relates to a software compensation color correction method for LED lamp beads. By means of a light splitting test system in a lamp bead packaging link, key parameter values such as the position and the wavelength of a light-emitting wafer of each lamp bead and a lamp bead collection temperature value are comprehensively recorded, optical correction values are measured and calculated, and the optical correction values are stored in registers in the lamp beads; the application device is powered on to output the target brightness value, and the lamp bead calls the optical correction value in the register to achieve accurate control over the color of the lamp bead. The method provided by the invention thoroughly breaks through the limitation of a traditional Bin scheme, 96% of brightness / chromaticity deviation can be eliminated, lamp beads of different batches under different working conditions have completely consistent optical characteristics, and the method is particularly suitable for scenes such as Micro-LED display, medical endoscopes and the like which have extremely strict requirements on color precision.

The invention discloses an image super-resolution reconstruction method based on multi-scale exposure normalization and double-branch feature aggregation, and relates to the technical field of image reconstruction, and the method comprises the steps: obtaining a to-be-processed image; constructing a super-resolution network model based on a Swin Transform architecture, performing feature reconstruction on the to-be-processed image through the super-resolution network model, and outputting a super-resolution image; wherein the super-resolution network model extracts shallow feature information of the to-be-processed image, exposure normalization processing is performed on the to-be-processed image based on the shallow feature information, and after color correction and feature enhancement are performed on exposure normalization features, the super-resolution image is mapped and reconstructed. According to the method, the problems of important detail missing, color distortion, structure distortion and the like in the image exposure correction process in an overexposure scene are solved, so that high-definition and high-resolution image reconstruction is realized.

The invention relates to the technical field of image processing, and discloses an HVI color space-based shadow removal method and system, a terminal and a storage medium, and the method comprises the steps: obtaining an RGB input image, converting the RGB input image to an HVI color space, and carrying out the decoupling of the RGB input image to obtain HV branch information and I branch information; designing a double-branch network structure, extracting color features in HV branch information by using an HV branch in the double-branch network structure, and extracting brightness features in I branch information by using an I branch in the double-branch network structure; fusing the color features and the brightness features by using an attention mechanism to obtain fusion information; and performing brightness adjustment and color correction on the fusion information to obtain an HVI component, mapping the HVI component back to an HVI color space, converting the HVI component into an RGB image, and outputting a shadow-free image. According to the method, the HVI color space and the double-branch architecture are introduced, so that the robustness and the accuracy of image shadow removal can be remarkably improved.

Systems and methods for color correction of images are described herein. An example method includes receiving, at a first time and a second time: first and second image data from an imaging device of a medical imaging system captured during a medical procedure; and first and second values of an operating parameter of a light source of the medical imaging system, wherein a color of light emitted from the light source is shifted based on the values of the operating parameter. A first color shift is determined based on the first value, and a color correction function is applied to the first image data to compensate for the first color shift to generate first color corrected image data. Second color-corrected image data is generated by determining a second color shift based on the second value and applying a color correction function to the second image data to compensate for the second color shift.

The invention provides an underwater structure crack detection method for underwater image enhancement of a turbid water area, and the method comprises the steps: collecting water quality parameters in real time through a plurality of sensors integrated at a front end, constructing an environment feature encoder in combination with a neural network, and mapping an environment state to a physical prior generation network to predict transmissivity and a channel attenuation coefficient. The prior information guides an image defogging and color recovery module, combines enhanced image definition feedback, dynamically optimizes network weight, and adopts synthesis and real data mixed training and loss function adaptive adjustment to improve the adaptability of the model to a complex underwater environment and the image enhancement effect. According to the invention, high-quality recovery and color correction of underwater images under different water body conditions are realized.

Display systems, media, and methods. Pixels of an image former form an image. Each pixel has a first color element to propagate a first amount of a first light having a first wavelength, based on a first electrical stimulus, and a second color element to propagate a second amount of a second light having a second wavelength, based on a second electrical stimulus. A display surface presents the image across a plurality of display surface locations. A processor executes instructions to scale the first electrical stimulus of each pixel of the image former by a first light scale factor, scale the second electrical stimulus of each pixel of the image former by a second light scale factor, and apply a pixel shading map to the image former to independently adjust, for each pixel of the display, the first amount relative to the second amount.

The invention discloses a color threshold adjustment method and system based on light brightness adaptive change. The method comprises the following steps: acquiring an image of any color; carrying out color correction on the acquired image so that the acquired image is restored to the real color of the object under different illumination; the corrected image is converted into a corresponding HSV color space, V channel information in the HSV image is extracted, a corresponding brightness image is obtained, and a brightness value is averaged; dynamically adjusting the threshold range of the color according to the average brightness value and dynamically adjusting the upper and lower limit values of the HSV color space of the color; segmenting the HSV image to obtain a regional mask of any color; carrying out filtering processing on a color region mask image in the processed binarized image to obtain a filtered color mask; and converting the originally collected image into a grayscale image, and performing masking operation on the grayscale image by using the filtered color mask to obtain a grayscale image only containing a color region. The method can effectively meet the actual requirements of industrial production.

The invention relates to the technical field of digital image processing, and discloses an image defogging method based on an atmospheric scattering model and color correction, and the method comprises the following steps: S1, carrying out the color correction of a foggy image with color cast; s2, calculating an atmospheric light value of the foggy image; s3, calculating the transmissivity value of the foggy image; s4, recovering the defogged image by adopting an atmospheric scattering model according to the atmospheric light value and the transmissivity value; and S5, solving a defogged image after adaptive brightness enhancement and adaptive contrast enhancement. According to the method, the color of the obtained defogged image can be recovered to be natural, the phenomena of color cast, incomplete defogging, detail information loss and overall darkness of the image are improved, the recovered defogged image better conforms to the perception of human eyes, meanwhile, the implementation process is simple, the time complexity is low, and better defogging efficiency is achieved.

The invention discloses a low-light image enhancement method based on multi-branch feature fusion. The method comprises the steps of obtaining low-light and normal-light images, and dividing training and test sets; processing the normal light image to obtain a single-channel color-enhanced, brightness-enhanced and texture-enhanced component graph; obtaining a multi-branch feature fusion low-light image enhancement neural network model; and inputting a to-be-measured low-light image into the model to enhance the backbone network, and outputting a restored image under a normal illumination condition. According to the invention, a color dynamic calibration module is provided, and a plug-and-play color correction normal form is provided for the model; an external source wound modulation module is provided, and key information can be focused while interference information such as noise is restrained. A brand new shared convolution is provided, so that the model can adaptively adjust the characteristic response intensity of each channel. An exogenous creative shared residual block structure is constructed, so that the enhanced backbone network can utilize the internal feature abstraction ability and the external auxiliary information guide advantage at the same time.

An image processing method, executed by an electronic device and comprising: performing object detection on an image to be processed, so as to determine a pixel-region of interest in the image to be processed; performing color temperature value estimation on the basis of a non-pixel-region of interest other than the pixel-region of interest in the image to be processed, so as to determine an estimated light source color temperature value of the image to be processed; on the basis of the estimated light source color temperature value of the image to be processed and historical light source color temperature values of reference images, determining a white balance gain for the image to be processed, wherein the reference images and the image to be processed are acquired by a same image acquisition device, the reference images comprising at least one of K image frames acquired by the image acquisition device before the image to be processed, and K being a positive integer; and, on the basis of the white balance gain for the image to be processed, performing color correction at least on the pixel-region of interest in the image to be processed.