66 results about "Luminance channel" patented technology

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

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.

A method comprising receiving an image block unit of a frame, the image block unit being predicted using a cross-component prediction mode and comprising samples in color channels comprising at least one chrominance channel and one luminance channel (900); determining intra prediction direction based on the samples of said luminance channels of the image block unit, samples in a neighborhood of the luminance channel of the block unit and / or samples in the neighborhood of the block unit (902); selecting a low-frequency nonseparable transform index for low-frequency non-separable transform using the determined intra prediction direction (904); and performing the low-frequency non-separable transform using the selected low-frequency non-separable transform index (906).

A method comprising receiving an image block unit of a frame, the image block unit comprising samples in a first chrominance channel, chrominance channel, a second chrominance channel and one luminance channel; defining co-located reference areas over luminance components of the luminance channel, chroma components of the first chrominance channel and chroma components of the second chrominance channel; performing motion-compensated prediction for the luminance component, the first chrominance component and the second chrominance component using reference frames and motion vectors; obtaining a first mapping function that maps the luminance component into the first chrominance component and a second mapping function that maps the luminance component into the second chrominance component using the co-located luminance prediction and the chrominance predictions; reconstructing a luminance residual; and obtaining a first chrominance prediction by using the first function and the luminance residual, and a second chrominance prediction by using the second function and the luminance residual; and obtaining a first chrominance prediction by using the first function and the luminance residual, a second chrominance prediction by using the second function and the luminance residual.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said color channels to produce reconstructed chrominance samples and reconstructed luminance samples; determining that a picture-level model list re-ordering is used; means for determining a reference picture; determining a list of cross-component models for the reference picture; generating a re-ordered list where the cross-component models of the determined list have been re-ordered; determining an index for a cross-component model; means for deriving the cross-component model from the re-ordered list with the given index; applying a first adaptive in-loop filter for the reconstructed luminance samples; and the derived cross-component model for the reconstructed luminance samples to obtain an additional input for reconstructing the chrominance samples.

The invention belongs to the technical field of image processing, and particularly relates to a dim light blurred image restoration method based on double-branch brightness-chrominance attention. The method comprises the following steps: acquiring a to-be-processed dark light blurred image, converting the to-be-processed dark light blurred image into a YUV color space, separating a Y brightness channel and a UV chromaticity channel, and obtaining a shallow brightness feature and a shallow chromaticity feature; inputting the shallow brightness features and the shallow chromaticity features into a pre-trained DB-LCANet network for processing to obtain deep brightness features and deep chromaticity features; obtaining a restored image according to the deep brightness feature and the deep chromaticity feature; the DB-LCANet network is composed of six DBSCAB modules which are connected in sequence; according to the method, the brightness information and the color information are processed separately, brightness enhancement in a new color space does not influence the color information any more, so that the problems of color distortion and excessive brightness improvement possibly caused in the dark light enhancement process are avoided, and the method has a good application prospect.

The invention provides an image display method and device, a storage medium and electronic equipment, and the method comprises the steps: converting a first image displayed on a screen of terminal equipment into a gray image, and then carrying out the Fourier transform operation of the gray image, and obtaining a first frequency spectrum image corresponding to the gray image. And enhancing the frequency intensity of each frequency in the first frequency spectrum image to obtain a second frequency spectrum image. And performing inverse Fourier transform operation on the second frequency spectrum image to obtain a gray level image corresponding to the second frequency spectrum image, and converting the gray level image corresponding to the second frequency spectrum image into a second image in an RGB color space. And updating the image displayed on the screen of the terminal device from the first image to the second image. According to the image display method provided by the embodiment of the invention, by enhancing the frequency intensity of each frequency in the frequency spectrum image corresponding to the brightness channel, the definition of image display can be improved, and the visual experience of a user is improved.

The application discloses a multi-stage information interactive weak light image enhancement method based on a double-channel normalized flow, and specific steps are as follows: step 1, converting a weak light RGB image to a YUV color space to obtain a luminance channel image Y and color channel images U and V; step 2, constructing an overall network, inputting the luminance channel image Y and the color channel images U and V obtained into the overall network to obtain corrected luminance channel image Y'', color channel image U'' and color channel image V''; and step 3, converting the corrected luminance channel image Y'', the color channel image U'' and the color channel image V'' obtained into an RGB image to obtain an enhanced RGB image. The method improves the enhancement effect on a weak light image.

According to the face image processing method and device, the storage medium and the computer equipment provided by the invention, after the to-be-processed face image is acquired, the face region in the face image is firstly determined, and the key points of the five sense organs are marked, so that a space coordinate basis of the key region is provided for subsequent processing; and then, a brightness channel image is separated from the face region, and the scene category of the face image is judged by quantifying the illumination distribution characteristics of the face region, so that a decision basis is provided for subsequent processing. For example, in a dark scene, five-sense-organ brightness hierarchical correction is performed to maintain skin pore-level textures while brightening; performing dynamic range compression in a strong backlight scene to balance light and shade transitions; limited histogram equalization is performed in a normal illumination scene to prevent over-sharpening or noisy explicit of the image. In addition, noise reduction processing, detail restoration and feature enhancement are sequentially carried out on the image after enhancement processing, and the finally generated image can greatly improve the recognition accuracy and the living body detection passing rate.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel (1200); reconstructing samples of said luminance channels of the image block unit (1202); determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more of reference samples in a neighboring block in current color channel / frame, in the neighboring of a co-located block in reference color channel / frame; and / or inside the co-located block in reference color channel / frame (1204); determining at least one fixed value to be subtracted from sample values of samples in said reference area prior to determining parameters of a cross-component prediction model (1206); predicting said target samples of at least one color channel of the image block unit using the determined parameters of the cross-component prediction model (1208); and adding said at least one fixed value to values of the predicted target samples of at least one color channel of the image block (1210).

A method comprising: receiving one or more image block units of a picture, the image block units comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said colors channels to produce reconstructed chrominance samples and reconstructed luminance samples; deriving an indication that picture-level cross-component model inheritance is used for adaptive in-loop filter chrominance update for the current picture; determining a reference picture from a set of candidate reference pictures; and applying, for said one or more image block units of the picture, the adaptive in-loop filter chrominance update parameters as used in co-located image block units of the reference picture.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said colors channels to produce reconstructed chrominance samples and reconstructed luminance samples; determining a list of cross-component models; determining an index in the list of cross-component models; deriving a cross-component model from the list with the given index; applying a first adaptive in-loop filter for the reconstructed luminance samples; applying the derived cross-component model for the reconstructed luminance samples to obtain an additional input for reconstruction of samples of at least one chrominance channel.

In accordance with various embodiments, an electronic device for colorizing a black and white image using a Generative Adversarial Network (GAN)-based model comprising a transformer block includes a processor, wherein the processor is set to: obtain a black and white image including only first information about a luminance channel; and generate a pseudo color image including only second information about a chrominance channel by applying the black and white image to the GAN-based model, the GAN-based model includes a generator network including a plurality of transformer blocks for color conversion, a plurality of convolution layers, and a plurality of transpose convolution layers, the plurality of transformer blocks each include a Depth Wise Convolution (DWC) layer, a first Layer Normalization (LN) layer, a Window-based Multi-head Self Attention (W-MSA) layer, a second LN layer, and a Colorization Feed Forward (CFF) block. Other various embodiments are possible.

A method comprising: receiving (1500) an image block unit of a frame, the image block unit comprising samples in color channels, wherein the color channels comprise at least one chrominance channel and one luminance channel; reconstructing (1502) samples of said luminance channels of the image block unit; determining (1504) a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more of reference samples in a current or a co-located block in a current color channel / frame or in a reference color channel / frame encoded using an intra block copy (IBC) method; determining (1506) a block vector indicating a spatial distance of an area of target samples to the reference area; and predicting (1508) said target samples of at least one color channel of the image block unit using the cross-component prediction model based on the reference samples in said reference area indicated by said block vector.

The present embodiment provides a method for compressing an additional channel other than a YUV channel. In the present embodiment, an image decoding device acquires a bitstream including a luma channel and a chroma channel of an image. The image decoding device reconstructs the luma channel and the chroma channel from the bitstream. The image decoding device identifies whether an additional channel is included in the bitstream. When the additional channel is included in the bitstream, the image decoding device reconstructs the additional channel from the bitstream.

The invention discloses an infrared multi-mode composite imaging system based on an APD detector, and the system achieves the quick conversion of three modes through a switchable optical assembly: in a passive imaging mode, the detector receives a thermal radiation signal emitted by a target; in the active gating mode, a laser is utilized to irradiate a target, and set target depth-of-field imaging is realized by controlling a time window of a detector; in the active 3D imaging mode, a time flight method is used, and distance information of a target is obtained by emitting laser and receiving reflection echoes of the laser. Meanwhile, a double-layer fusion framework based on adaptive weight is designed, spatial distance information in an active 3D imaging mode is coded to a chrominance channel in a YCbCr color space, heat radiation information and object contour details in a passive infrared mode are coded to a brightness channel, and an image fusing two-dimensional heat information and three-dimensional space information is output.

This invention discloses a real-time video clarity restoration system based on depth artifact recognition, belonging to the field of computer vision technology. Specifically, it includes: a decoding extraction module, a feature extraction module, a mask generation module, a dynamic allocation module, and an image reconstruction module. The system decodes the original video stream, extracting the luminance and chrominance channel data of each video frame. By comparing the transform coefficient information obtained from the decoding side with cross-frame chrominance data, a dual-channel fused chrominance residual feature map is calculated, constructing a multi-channel input feature tensor. Based on a lightweight shallow convolutional neural network, a pixel-wise artifact probability mask is output through cross-channel feature fusion and nonlinear mapping. The video frame is divided into high-probability artifact regions and low-probability smooth regions, and these regions are processed separately. Using spatial fusion weights, the restored region features are weighted and linearly combined with the original video frame data, and a high-definition video frame is output after processing.

The invention relates to the technical field of image enhancement, in particular to a film and television production system based on ai intelligence and audio-video simulation restoration technology, which comprises a pixel sequence extraction module, an illumination path generation module, a dynamic filling and sorting module, a structure track integration module and a direction restoration calculation module. According to the method, the brightness channel value of the target area in the continuous frames is extracted, the inter-frame gradient map is constructed, the brightness change trend is accurately described, the path set with direction coherence is established by combining the angle difference comparison between the edge gray level direction and the adjacent frame brightness direction, the recovery accuracy of the shielded area is improved, and the method has the advantages of being simple in structure and convenient to operate. Establishing a scoring mechanism according to the brightness amplitude and the change rate, optimizing a pixel filling sequence, avoiding brightness information dislocation, screening and combining the difference value of the included angle between the brightness trend and the direction of a grid area, enhancing the structural continuity and the naturalness of brightness transition, and reconstructing a brightness change sequence based on track guidance. And the restoration effect of the shielding area is more realistic and has structural harmony.

The present application belongs to the field of computer vision and image recognition technology, and relates to a ship navigation obstacle visual detection method and system. The method comprises: converting the initial image of the water area in front of the ship to the LAB color space to separate the brightness and chrominance channels; performing CLAHE processing on the brightness channel, calculating the absolute difference value feature, and combining the pixel value to extract the local brightness distortion degree; extracting the color Euclidean distance and the local brightness distortion degree for fusion to obtain the scene chrominance response feature; based on the theoretical maximum normalization constant, combining the local brightness distortion degree to generate a dynamic gain compensation term to construct a color shift compensation coefficient; and using the compensation coefficient to compensate the chrominance channel, and converting the enhanced brightness image back to RGB to generate a global enhanced image. The present application can effectively separate the brightness and chrominance to avoid mutual influence, scientifically analyze the color protection demand, effectively correct the color shift to maintain the original characteristics, and improve the accuracy and safety of ship navigation obstacle visual detection.

The application discloses an image preprocessing neural network model, a training method, a near-eye display method, a device, equipment and a medium. The model comprises: a color cast correction branch configured to correct color cast of a to-be-displayed image based on features of independent luminance channels in a color space image to obtain a first corrected image; wherein the color space image is converted from the to-be-displayed image, and the color space image comprises at least one independent luminance channel feature and at least one color channel feature; a pre-correction branch configured to pre-correct the to-be-displayed image based on optical characteristics to obtain a second corrected image; and a fusion module configured to fuse the first corrected image and the second corrected image to obtain a fused image. The scheme disclosed by the application obtains the fused image which guarantees the pre-correction effect and weakens the color cast degree through the double-branch architecture and the fusion module, guarantees the pre-correction effect and the color consistency, and improves the subjective experience of a user.

The invention relates to the technical field of integrity verification, in particular to a video and audio file migration integrity verification method, which comprises the following steps: acquiring a synchronous frame extraction pixel sequence, establishing mapping to generate feature data, extracting time, marking a stable section, extracting brightness residual error, judging the direction to be consistent, and analyzing chromaticity variance to generate the stable section. According to the method and the device, the brightness and chroma pixel value sequences are extracted from the synchronous frames in the program packaging file and the broadcast rendering frame, and the mapping relation is established, so that frame-by-frame accurate comparison can be realized, the synchronous state is identified in combination with the time sequence difference trend, the inter-frame comparison precision is improved, and the accuracy of the inter-frame comparison is improved. Through the conjoint analysis of the brightness channel residual direction and the chrominance channel variance distribution, the image anomaly recognition capability is enhanced, and the frame-level consistency result is extracted based on the multi-dimensional residual feature intersection, so that the video and audio integrity verification without depending on a unified decoder is realized, and the real-time performance and fine granularity of quality assurance are enhanced.

The present application relates to the technical field of computer vision, and particularly relates to an underwater non-uniform illumination image enhancement method, system, device and medium, comprising: correcting an obtained luminance channel standard image to obtain a luminance correction image by using an adaptive gamma correction algorithm and an illumination distribution map; synthesizing a color image according to the luminance correction image to obtain UV channel values of each pixel point of the color image in a YUV color space; determining a Y channel white point position in the YUV color space according to the UV channel values of all pixel points and a preset UV highlight threshold; performing white balance adjustment on each channel of the color image at the Y channel white point position to obtain an enhanced image; and performing wavelet denoising processing on the enhanced image to obtain a denoised image. The method effectively improves the quality of the underwater non-uniform illumination image, can be applied to various complex underwater scenes, and improves the reliability and stability of the enhancement effect.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel (1100); reconstructing samples of said luminance channels of the image block unit (1102); determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more of reference samples in a neighboring block in current color channel / frame, in the neighboring of a co-located block in reference color channel / frame; and / or inside the co-located block in reference color channel / frame (1104); determining filter coefficients of a filter for said predicting based the reference samples and a shape of the filter (1106); reconstructing the target samples of at least one color channel of the image block unit using said prediction based on the samples of said reference area and the filter coefficients (1108); computing an autocorrelation matrix for the reconstructed target samples of the image block unit (1110); computing autocorrelation matrices for a plurality of neighboring image blocks units of said image block unit (1112); mixing the autocorrelation matrices of each of the plurality of neighboring image blocks units separately with the autocorrelation matrix of said image block unit (1114); and storing the mixed autocorrelation matrices for said image block unit (1116).

Methods and apparatus for image compression / decompression using tensor product B-spline (TPB) representations. According to an example embodiment, an image-compression method includes generating a plurality of TPB models representing an image, including a TPB model for approximating a spatial reshaping function and a plurality of patch-wise TPB models for estimating the image signal. In some examples, the plurality of patch-wise TPB models includes a plurality of luma-channel TPB models and a plurality of chroma-channel TPB models. The spatial reshaping function is configured to shift a non-uniform distribution of local content complexity in the image toward being more uniform. The method also includes generating a metadata stream carrying metadata representing sets of coefficients of various TPB models. At least a portion of the metadata is generated by quantizing the corresponding coefficients to a smaller number of bits and applying arithmetic coding to the quantized coefficients.

A method comprising: receiving one or more image block units of a picture, the image block units comprising samples in color channels comprising at least one chrominance channel and one luminance channel; determining a block of chrominance samples to be predicted; determining an intermediate intra prediction mode to be applied for said block of chrominance samples; deriving intermediate intra prediction samples for said block of chrominance samples using the intermediate intra prediction mode; deriving first cross-component model parameters using the intermediate intra prediction samples and co-located luminance samples; deriving first chroma prediction samples for said block of chrominance samples based on the derived first cross-component model parameters; deriving second cross-component model parameters using adjacent reconstructed samples as reference samples; deriving second chroma prediction samples for said block of chrominance samples based on the derived second cross-component model parameters; and blending the first chroma prediction samples and the second chroma prediction samples to obtain final chroma prediction samples.

A method comprising: receiving one or more image block units of a picture, the image block units comprising samples in color channels comprising at least one chrominance channel and one luminance channel; determining a block of chrominance samples to be predicted; determining an intermediate intra prediction mode to be applied for said block of chrominance samples; deriving intermediate intra prediction samples for said block of chrominance samples using the intermediate intra prediction mode; deriving cross-component model parameters using the intermediate intra prediction samples and co-located luminance samples, and deriving chroma prediction samples for said block of chrominance samples based on the derived cross-component model parameters.

This invention relates to the field of image processing technology, and more specifically, to a visual inspection method and system for grade sorting of molybdenum oxide products. The method includes: acquiring a color image of the molybdenum oxide product and converting the color image into a color space containing a luminance channel and at least one chrominance channel; in response to dividing the luminance value of each pixel by the corresponding initial illumination value to obtain a reflectance residual, mapping the reflectance residual to a purity confidence score. This invention first guides the construction of a pure illumination map by calculating the purity confidence score, avoiding interference from impurity pixels on illumination estimation; then, it introduces a dynamic compensation gain based on the similarity between pixel chrominance and prior chrominance, making the compensation process selective, strongly brightening shadow areas with colors consistent with qualified products, while only weakly compensating for impurity areas with abnormal colors.

The invention relates to the technical field of signal-to-noise ratio detection, in particular to a medical endoscope signal-to-noise ratio detection method, device and equipment and a storage medium. Obtaining brightness channel average quartile difference data, first color difference channel average quartile difference data and second color difference channel average quartile difference data from the image data according to a preset first percentile and a preset second percentile; calculating the average quartile difference data of the brightness channel, the average quartile difference data of the first color difference channel and the average quartile difference data of the second color difference channel according to a preset total noise synthesis formula to obtain total noise data; drawing a signal-to-noise ratio curve according to the total noise data; according to the scheme, multi-frame acquisition is combined with a clinical working distance, a uniform gray scale region and slight focusing blur, the noise data purity is guaranteed, the quartile difference can shield an abnormal value, non-Gaussian noise is adapted, three-channel noise is fused to meet the requirement, the extracted signal noise is traceable, and the evaluation accuracy is improved.

The present application provides a kind of bare rock information processing method, device and electronic equipment, target area image can be obtained, target area image is the image that unmanned aerial vehicle is photographed when being in first flight height, facing target bare rock distribution area;First bare rock distribution information is obtained based on the chroma channel image corresponding to target area image;Second bare rock distribution information is obtained based on the luminance channel image corresponding to target area image;Target bare rock distribution information is obtained based on first bare rock distribution information and second bare rock distribution information.The present application can obtain target area image, corresponding chroma channel image and luminance channel image are obtained using image processing method, first bare rock distribution information and second bare rock distribution information are extracted, and target bare rock distribution information is obtained based on first bare rock distribution information and second bare rock distribution information.The present application can effectively reduce the human resources consumed in the process of determining bare rock distribution information, and effectively improve processing efficiency.

This disclosure provides an image processing method, relating to the field of artificial intelligence technology, particularly augmented reality, virtual reality, computer vision, and deep learning, applicable to scenarios such as metaverse and virtual digital humans. The specific implementation involves: encoding the initial luminance channel image of the original image to obtain a first encoding result of the initial luminance channel image, wherein the first encoding result includes first luminance image features; obtaining a target luminance channel image based on the target spherical harmonic coefficients and the first encoding result; and fusing the target luminance channel image with the initial chroma channel image of the original image to obtain the target image. This disclosure also provides a training method, apparatus, electronic device, and storage medium for a deep learning model.