68 results about "Resolution recovery" patented technology

The invention discloses a low-illumination image enhancement method based on a YCbCr space and a Fourier frequency domain, and the method comprises the steps: extracting a brightness component of a low-illumination image in the YCbCr space, and constructing a training set together with a normal-illumination image and the low-illumination image; constructing a low-illumination image enhancement network model, and inputting the training set into the model for training; constructing a loss function between the amplitude enhancement network branch and the normal illumination image as well as between the phase enhancement network branch and the normal illumination image, and adjusting model parameters by minimizing the loss function to obtain an optimization model; and inputting a to-be-enhanced low-illumination image into the optimization model for image enhancement. According to the method, the YCbCr brightness component and the amplitude information of the Fourier frequency domain are combined, and the Transform is utilized to enhance the phase information, so that the visual quality of the low-illumination image is remarkably improved, the visibility and definition of the image are improved, and the method has wide application prospects in tasks of image enhancement, denoising, super-resolution recovery and the like.

The invention discloses a pelvic floor rehabilitation effect image contrast enhancement method, and belongs to the field of image processing. The objective of the invention is to improve the quality of pelvic floor X-ray images and the efficiency and accuracy of clinical diagnosis. According to the method, through collection compression and enhancement recovery modular design, whole-process image quality optimization is realized; the acquisition compression module retains low-contrast area information through dynamic resolution adjustment and area sensing compression, and the transmission bandwidth requirement is reduced; the enhancement and recovery module recovers the image resolution by using the mixed super-resolution and enhances the local and global contrast; in addition, the method further comprises a degradation modeling module, a loss function and training strategy module and a system integration module, and the enhancement effect of the generation network on the low-contrast image is optimized through multi-task learning training. According to the method, the defects of image degradation and noise processing are overcome, the balance between the image enhancement effect and diagnosis correlation is optimized by introducing a multi-task loss function, and powerful support is provided for clinical diagnosis and treatment.

The invention provides a remote sensing image super-resolution recovery method based on a GAN neural network. The remote sensing image super-resolution recovery method comprises the following steps: constructing a sub-region uniform clipping module to generate a corresponding low-resolution training pair for a high-resolution image; building a GAN neural network for adversarial generation; an inverse transformation image decoder is constructed to serve as a generator network fitting super-resolution process in a GAN network framework, and then a discriminator network is used for discriminating the true degree of a generated picture for gradient descent training of the GAN network. Some embodiments of the invention realize remote sensing image super-resolution reconstruction based on the adversarial neural network and local semantic information fusion, and have the characteristics of general applicability to image content, generation rationality and diversity, robustness and the like.

The invention discloses a channel estimation method based on a super-resolution network. According to the invention, the time-frequency grid of the channel response is modeled as a known 2D image only at the pilot position. A channel grid with a plurality of pilot frequencies is regarded as a low-resolution image, and an estimated channel is regarded as a high-resolution image. And extracting target LR image features by using the constructed convolutional neural network, and performing interpolation processing between known pilot frequency positions, thereby performing super-resolution recovery on a channel image to become an HR image, and realizing channel estimation operation. The method comprises the following steps: constructing a super-resolution neural network; generating a training data set; training the super-resolution neural network; testing the network performance by using the tested data set; according to the method, the super-resolution neural network is used for learning the channel matrix characteristics, the accuracy and robustness of channel estimation are greatly improved, and the performance is improved.

The invention discloses an urban green plant identification method and device, a medium and equipment, and relates to the technical field of image identification, and the method comprises the steps: obtaining a to-be-identified remote sensing image; deep features in the remote sensing image are extracted, multi-scale spectral features in the deep features are obtained, the deep features and the multi-scale spectral features are fused, and a multi-spectral feature map is generated; wherein the deep features comprise color features and texture features; extracting strip features and multi-scale shape features in the remote sensing image, and fusing the strip features and the multi-scale shape features to generate a multi-shape feature map; performing weighted fusion on weights corresponding to the multi-spectral feature map and the multi-shape feature map to determine a fused feature map; and recovering the resolution of the fused feature map to the size of the original image, and classifying the feature map with the recovered resolution to obtain a final urban green land classification result.

The invention discloses a power line image super-resolution recovery system and method for power transmission line patrol, and relates to the technical field of unmanned aerial vehicle image processing. The system provided by the invention comprises three core modules, namely a physical perception generator, a differentiable physical discriminator and a spatial transformation network (STN), wherein the generator is combined with a U-Net and a residual structure, and the output is ensured to accord with the geometry and material characteristics of a power line through physical constraint loss; the discriminator adopts a double-branch structure, and synchronously evaluates image authenticity and physical rationality; and the STN module corrects perspective distortion by using the pose data of the unmanned aerial vehicle to realize geometric alignment of the power line. The system integrates advanced components such as Swin Transformer, ViT and differentiable RANSAC, supports end-to-end processing from original Bayer data to a high-definition image, and outputs a defect detection confidence map with enhanced physical constraints. Compared with a traditional data driving method, the method strictly guarantees the physical credibility of a reconstruction result while improving the resolution, and is suitable for a power line inspection task in a complex environment.

The invention relates to a high-fidelity generative video stream transmission system based on a visual base model, which belongs to the field of image communication, and is characterized in that a visual enhancement-oriented generative codec is designed, and high-fidelity video reconstruction under a high compression ratio is realized through an asymmetric space-time compression strategy and time sequence consistency enhancement; a resolution scaling module is provided, the calculation complexity is remarkably reduced through a video super-resolution recovery module of adaptive resolution control and joint optimization, and real-time high-definition video processing is achieved; and constructing a network adaptive video stream transmission controller, an intelligent token discarding mechanism based on semantic importance and a mixed packet loss processing strategy to realize code rate scalable control and robust transmission under network fluctuation.

The present disclosure relates to methods, apparatus, systems, and non-transitory computer-readable storage media for video coding using super-resolution restoration with residual frame coding. According to some examples, a computer-implemented method includes receiving a coded frame of a video; performing a video coding on the coded frame of the video to generate a resultant for the coded frame at a second lower resolution than a first resolution; upsampling the resultant in at least a vertical direction to a higher resolution than the second lower resolution to generate an upsampled resultant; generating a decoded frame based on at least the upsampled resultant; and transmitting the decoded frame to a frame buffer or to a display device.

An information processing system includes a processor. The trained model is trained to resolution recover a low resolution training image generated by low resolution processing performed on a high resolution training image to a high resolution training image that represents a high resolution image captured with a predetermined object through the first imaging system. The low resolution processing represents processing that generates a low resolution image as if captured with the predetermined object through the second imaging system and processing that simulates the second imaging method, and includes processing that simulates a resolution characteristic of an optical system of the second imaging system. The processor uses the trained model to resolution recover the processing target image captured through a second imaging system to an image having a resolution at which the first imaging system performs imaging.

This application discloses a landslide segmentation method, apparatus, equipment, and medium based on hybrid Mamba and frequency domain calibration, relating to the field of landslide disaster detection technology. The method involves fusing optical satellite imagery with slope and lithological topographic constraints, preprocessing the data, and then inputting it into a model containing a hybrid Mamba encoder, a multi-scale adaptive gating module, and a progressive frequency domain calibration decoder for processing. Through multi-stage hybrid coding, contextual aggregation modulation, and progressive resolution recovery, accurate landslide segmentation is achieved, effectively improving global feature modeling capabilities, enhancing the identification accuracy of multi-scale and boundary-ambiguous landslides, balancing lightweight design and robustness, and better adapting to the actual monitoring needs of highway landslides.

The present invention provides an ATFormer architecture for medical image segmentation, comprising a patch partitioning layer, an encoder, a bottleneck layer, a decoder, and a linear projection layer; the patch partitioning layer divides an input medical image into non-overlapping patch images; the encoder generates feature maps at different resolutions from the non-overlapping patch images, wherein the resolution of the feature maps is reduced and the feature dimension is increased; the bottleneck layer performs deep feature learning on the feature maps; the decoder gradually upsamples the feature maps at different resolutions from the encoder under the joint action of a progressive guided fusion (PGF) module, wherein the resolution of the upsampled feature maps is increased and the feature dimension is reduced, and the resolution of the feature maps is restored to the original resolution; and the linear projection layer outputs the feature maps output by the decoder as pixel-by-pixel segmentation prediction results. The present invention provides a new ATFormer architecture that can be used for medical image segmentation and provides accurate segmentation results.

A device for image processing of a rear view camera comprising: an image signal processor that generates an original rear view image captured by a rear view camera to generate a rear view image; a first processing unit that corrects a distortion phenomenon of the rear view image to create a distortion-corrected image and produces it as an output image displayable by a display device; and a second processing unit that reduces the resolution of the reversing image, to create a reduced reversing image, and temporarily stores the reduced reversing image, and upon receiving an operation control signal, corrects the distortion phenomenon of the temporarily stored reduced reversing image, and then restores the resolution to the resolution of the reversing image, and creates an output reversing image; and an image supplement processing unit that supplies the output image created by the first processing unit to the display device and judges the effectiveness of the output image based on an adjustment signal transmitted from the outside after an initial operation, and generates the operation control signal, so that the second processing unit works when the output image is not effective.

This invention relates to a dual-branch remote sensing image semantic segmentation method and system based on Mamba and adaptive convolution, belonging to the fields of remote sensing image semantic segmentation and artificial intelligence technology. This method aims to solve the problems of existing technologies, such as difficulty in coordinating global dependencies and local texture information, low segmentation accuracy at multiple scales, poor multi-band adaptability, and low training efficiency. The invention includes using a multispectral adaptive processor for spectral grouping and attention enhancement, inputting the data into a dual-path backbone network after downsampling through a convolutional backbone. The global path uses a Mamba module to achieve linear complexity global modeling, while the local path uses adaptive convolution to dynamically adjust weights to capture details. A multi-head cross-attention fusion module then enables bidirectional interaction of multi-scale features, and a feature pyramid network performs cross-scale optimization and resolution restoration. This invention significantly improves segmentation accuracy, reduces computational burden, and adapts to the real-time application requirements of high-resolution remote sensing images.

The application discloses an underwater lining plate crack detection method and device based on image recognition, and relates to the technical field of underwater lining plate cracks. Standardized underwater lining plate image data is obtained; an initial spatial feature map is output; enhanced sequence feature representation is obtained; a global context enhanced feature map is generated; a first resolution recovery stage, a second resolution recovery stage and a third resolution recovery stage are sequentially executed; a first-level high-resolution feature map is generated; a second-level high-resolution feature map is generated; a third-level high-resolution feature map after adaptive rearrangement is generated; a crack segmentation prediction map is generated; a connected region label result is output; and underwater lining plate crack detection report data is output. The application can more completely express the long-distance continuous structure characteristics of underwater lining plate cracks, improve the identification capability of weak cracks, intermittent cracks and low-contrast cracks, and reduce the occurrence probability of crack breaking, missing detection and local false segmentation.

The present invention discloses a method for video deinterlacing and super-resolution based on a cyclic Unet network. The method of the present invention is a multi-frame input and multi-frame output adaptive video deinterlacing based on a cyclic Unet neural network. Each time, the current two adjacent frames of data and the two-field deinterlacing prediction data predicted in the previous time are input, and the above data are input into the Unet neural network, and the two-field deinterlacing data predicted in the current time and the deinterlacing data restored in combination with the input data are output. If the input is standard definition data and the output requires high-definition data, the deinterlacing data is input into the SRnet (super-resolution restoration network) to output high-definition deinterlacing data. If no data conversion is required, the deinterlacing data is directly output. The cyclic Unet neural network of the present invention has a powerful video deinterlacing restoration capability, and can adaptively process interlaced data of different scenes without scene judgment to obtain an ideal deinterlacing effect.

A deep learning-based automatic segmentation method for diabetic retinopathy fundus images includes the following steps: S1 resizing the diabetic retinopathy fundus image to 512*512, S2 starting image preprocessing, S3 passing the preprocessed image into a network model for node feature initialization, S4 feature transfer and fusion, S5 each node passing features to the upper layer node, segmenting the vascular structure in the image through deconvolution / interpolation / depooling methods, and when the last node of the first layer completes feature fusion, the resolution is restored to the same size as the original input image. S6 After the vascular structure segmentation is completed, a grayscale image of the vascular structure segmentation is output through a single layer of convolution. The method provides automatic segmentation of fundus vascular structures for medical assistance systems, solving the problems of slow image reading, unclear vascular ends, and incomplete segmentation caused by the easy loss of new small blood vessels. The present invention has the advantages of automatic extraction of fundus blood vessels, low computational resource consumption, and high segmentation accuracy.

The application discloses a kind of based on multi-scale feature refinement network's image super-resolution construction method, comprising the following steps: step one: using DIV2K dataset constructs high-resolution and low-resolution image pair, and the generalization ability of network model to diversified image features is improved by data enhancement technology;Step two: construct multi-scale feature refinement network: the network includes detail extraction engine, global multi-scale context module and feature refinement module;Step three: the parameter of multi-scale feature refinement network model is optimized by minimizing L1 loss function to train;Step four: using the network model trained to low-resolution image carries out super-resolution reconstruction, generates high-resolution image output;The model is evaluated on test dataset.The application introduces detail extraction engine (DEE), global multi-scale context module (GMCM) and feature refinement module (FRM), enhances cross-region feature interaction and the limitation in the aspect of detail in single image super-resolution recovery.

The application discloses a gas diffusion layer fiber binder segmentation method based on global and local feature fusion, and aims to accurately segment three types of regions of carbon fibers and phenolic resin binders in a gas diffusion layer in a SEM image. The method first collects SEM images of the gas diffusion layer with different phenolic resin loadings through an SEM (scanning electron microscope), and obtains an image data set after pretreatment, manual labeling and data enhancement and the like; then, an improved neural network model containing a GLIB global-local aggregation module, an LBWA boundary weight learning feature interaction module and an LKAT large kernel convolution attention module is constructed, global features are extracted by using a Transformer encoder, key region feature expression is enhanced in combination with multiple modules, multi-scale feature fusion and resolution recovery are completed through a decoder, and finally, a segmentation result is output.

The embodiment of the invention provides a wafer image matching method and device and a computer storage medium, and the method comprises the steps: firstly carrying out the down-sampling of an obtained to-be-detected and template image, and rapidly calculating an initial matching position in a low-resolution space, so as to greatly reduce a search range; performing up-sampling on the low-resolution to-be-detected image by using a pre-trained neural network, and reconstructing a detail-enhanced high-resolution image; and finally, performing fine matching in an initial position neighborhood based on the high-quality image, and determining an optimal sub-pixel position. According to the method, the search space is efficiently compressed through low-resolution rough matching, the overall calculation complexity is remarkably reduced, and the speed is increased; meanwhile, by means of super-resolution recovery driven by the neural network, the problem that details of a traditional interpolation method are fuzzy is effectively solved, high-fidelity image data are provided for fine matching, and therefore the sub-pixel-level matching precision is guaranteed.

The application discloses a video super-resolution method based on space-time convolution attention, and is specifically implemented according to the following steps: step 1, converting an input high-resolution video sequence into a low-resolution video sequence, so as to obtain a low-resolution video set; step 2, constructing a deep neural network structure for video super-resolution; step 3, training the network based on paired video data; step 4, video super-resolution reconstruction, so as to realize super-resolution recovery of the input video sequence. The application solves the problem in the prior art that the network is regarded as a black box, performance is improved by increasing the network depth and parameter scale, the rapid growth of the calculation complexity is ignored, and the efficiency and the reconstruction quality are difficult to be considered.

The invention provides an image data processing method and device and electronic equipment, and belongs to the technical field of image data processing, and the method comprises the steps: obtaining a resolution reduction image of an original image, and original image data of the original image in a first preset region; performing sequence-variable combination processing on the resolution-reduced image to generate an intermediate image of which the resolution is the same as that of the original image; wherein the combination processing comprises fuzzy processing and resolution recovery processing; replacing the image data corresponding to the first preset area in the intermediate image with the original image data to generate a target image; according to the technical scheme, the data transmission amount of the display device when the display device displays the high-definition image can be reduced, key information of the original image is reserved in a partition processing mode, and the experience feeling of a user is improved.

The invention is suitable for the technical field of video transmission, and provides a video stream transmission method and system, a video stream sending end and a video stream receiving end, and the method comprises the steps that the video stream sending end obtains video original data; performing compression processing on the plurality of original video frames according to a preset compression condition to obtain video compression data corresponding to the video original data, and sending the video compression data to a video stream receiving end; the video stream receiving end receives the video compressed data sent by the video stream sending end; performing resolution recovery processing on the plurality of compressed video frames through fast bilinear interpolation to obtain a first recovered video frame; performing channel dimension splicing processing on the plurality of first recovered video frames and the initial Gaussian noise to obtain a splicing result; and performing image recovery processing on the splicing result through a diffusion recovery model to obtain video recovery data corresponding to the video compression data. The video stream compression ratio of the video stream sending end can be maximized, and the video stream recovery quality of the video stream receiving end can be improved.

The invention discloses a deep hierarchical multi-semantic data processing method based on cooperation of a dynamic cobweb and a loss function, which can effectively recover the spatial resolution and detail information of an image and improve the precision and robustness of a segmentation result. The method comprises the steps that feature information of different scales is captured through a multi-scale residual block, a cobweb structure is adopted to represent a potential hierarchical dependency relationship between semantic categories, and weights are associated through matrix formalization; edge information of the detail part is extracted and enhanced through an edge enhancement module, and the recognition capability of a target boundary is improved; global context information of different scales is captured through hole convolution kernels of different sizes in an ASPP module, and the global feature capability of the model is improved; performing layer-by-layer spatial resolution recovery on the feature map by using a multi-scale residual block to extract feature information, and splicing the feature information with edge features from an encoder through an attention gate; and cooperatively adjusting the cobweb loss by using the optimized loss function after the dynamic adjustment is defined.

A source video is encoded in a streaming protocol comprising a normal stream and a companion stream. The normal stream contains I-frames and predicted frames at high resolution. The companion stream contains frames at a lower resolution. When needed, an I-frame from the companion stream may be decoded and upscaled from the second resolution to the first resolution and injected into a decoded picture buffer. At an interval corresponding to an amount of time that is shorter than an interval between I-frames in the normal stream, a first frame is downscaled to the second resolution and then upscaled again to the first resolution. The next frame is then encoded with reference to the upscaled frame such that an output stream recovers to the first resolution based on the second frame prior to receipt of the next I-frame in the normal stream.

The application provides an interventional medical instrument image segmentation method and system, comprising the following steps: acquiring DSA image data from an interventional surgery case database system and dividing the DSA image data into a training set and a test set; building a Unet model, converting an input picture into a semantic feature map through an Encoder module, converting the semantic feature map into a binary classification result through a Decoder module, completing resolution recovery, and obtaining a final segmentation result map; defining a weight function during the training process; defining a new energy function in combination with cross entropy and softmax; and fusing and mapping the segmentation result of the model to a DSA original map and highlighting the DSA original map. The application greatly improves the precision and stability of surgical operation by means of computer and robot technology, and can effectively reduce the harm of radiation to interventional doctors and the probability of intraoperative accidents.

The invention discloses a sock production line defect monitoring method and system based on image recognition, and relates to the technical field of industrial defect detection.The sock production line defect monitoring method comprises the steps that an elastic deformation manifold is constructed based on entanglement feature vectors, and a Lagrange function is established on the elastic deformation manifold; solving a differential homeomorphic mapping function on the elastic deformation manifold through a Lagrange function, and performing deformation compensation on the entanglement feature vector according to the differential homeomorphic mapping function; performing multi-scale feature extraction on the entangled feature vector after deformation compensation by using a separable quantum convolution kernel to obtain a defect feature map, and performing spatial resolution recovery on the defect feature map by using a deconvolution layer to generate a defect type and a position coordinate; according to the method, by constructing the elastic deformation manifold and solving the differential homeomorphic mapping function, the spatial continuity characteristics of deformation are captured, coordinate transformation of entangled characteristic vectors is achieved, and the problem that non-rigid deformation of socks in high-speed movement lacks a compensation mechanism is solved.

A technique is disclosed for analyzing and displaying the extent to which the images and structures inferred by a physically seeded multiscale network correspond to genuine resolution improvement and the extent to which they correspond to the hallucination of realistic looking structures. A selected reconstruction generated using a trained neural network is compared against a baseline representation (e.g., a baseline reconstruction) by calculating image similarity metrics between progressing spatially filtered versions of selected reconstruction. As the amount of spatial filtering increases, at some point the image similarity metrics will reach an extrema (e.g., a lowest distance between the images). At that extrema, the parameter(s) of the spatial filter can be used to identify a resolution score (e.g., a length scale) associated with that extrema. The resolution score is indicative of an amount of resolution recovery associated with the trained neural network with respect to the baseline.

The invention provides methods and systems pertaining to a method for recovering resolution of spectral base images acquired using spectral CT imaging techniques. In particular, embodiments aim to provide a method for generating resolution recovered spectral base images of a field of view of a high resolution based on low-resolution spectral base images of the field of view of a first resolution and a high resolution CT image of the field of view of a high resolution.

The application discloses a kind of urban green plant identification method, device, medium and equipment, it is related to image recognition technical field, including obtaining remote sensing image to be identified;Deep feature in remote sensing image is extracted, obtain multiscale spectral feature in deep feature, deep feature and multiscale spectral feature are fused, generate multispectral feature map;Wherein, the deep feature includes color feature and texture feature;Strip feature and multiscale shape feature in remote sensing image are extracted, and strip feature and multiscale shape feature are fused, generate multi-shape feature map;By corresponding weight of multispectral feature map and multi-shape feature map is weighted fusion, determine the feature map after fusion;The resolution of the feature map after fusion is restored to original image size, then the feature map of recovery resolution is classified, obtains the final urban green classification result.

Techniques are disclosed for analyzing and displaying the extent to which images and structures inferred by a physical seed multi-scale network correspond to true resolution improvements and their extent to which they correspond to illusions of realistic appearance structures. A selected reconstruction generated using a trained neural network is compared to a baseline representation (e.g., a baseline reconstruction) by calculating an image similarity measure between progressive spatially filtered versions of the selected reconstruction. As the amount of spatial filtering increases, the image similarity metric will reach an extreme value (e.g., the lowest distance between the images) at a moment. At the extremum, a parameter of a spatial filter can be used to identify a resolution score (e.g., a length scale) associated with the extremum. The resolution score indicates a resolution recovery amount relative to a baseline associated with the trained neural network.