972 results about "Inpainting" patented technology

The invention discloses a denoising network model based on a diffusion model and an oracle rubbing image restoration method, which can improve the universality of the restoration method, improve the diversity of restoration results, reduce the calculation overhead and reduce the manual dependence. The denoising network model is adjusted based on an existing U-Net network and comprises a convolutional trunk network, an encoder, a middle layer, a decoder, a deep convolutional layer and a feedforward neural network which are connected in sequence; segmenting the input image by the convolutional trunk network; the encoder comprises a plurality of groups of cascaded paired visual state space blocks and image merging blocks; the decoder comprises a plurality of groups of cascaded paired visual state space blocks and image expansion blocks; performing down-sampling and up-sampling operations on the image merging block and the image expansion block respectively; the visual state space block adopts the architecture of an existing Mama module, and a two-dimensional selective scanning module is selected to replace an S6 module of the Mama module.

The invention belongs to the technical field of digital restoration of computer vision and cultural heritage, and particularly relates to a traditional picture restoration method fusing low-resolution prior and efficient visual selection, which comprises the following steps: constructing a multi-source image data set, taking images in the multi-source image data set as high-resolution images, preprocessing the high-resolution images to obtain low-resolution images, and carrying out high-resolution priori and high-efficiency visual selection on the low-resolution images. The high-resolution image and the low-resolution image are respectively masked to generate simulated damage mask images, and the simulated damage mask images comprise a regular damage mask image and an irregular damage mask image; taking the multi-source image data set and the preprocessed multi-source image data set as training data, and training a multi-source image model; the dual-stage repair network comprises a coarse repair network and a fine repair network; according to the method, the problems of structural semantic loss, high priori information dependency and insufficient global and local coordination when an existing image restoration method is used for processing a complex scene and a large-range missing region are solved.

The invention discloses an image restoration method, an image restoration device and a storage medium, which are used for improving the structure restoration precision and the detail restoration capability of image restoration. The method comprises the following steps: acquiring multi-dimensional inertial data in real time; performing frequency domain analysis on the multi-dimensional inertial data by adopting sliding window short-time Fourier transform to obtain vibration intensity; if the vibration intensity does not exceed the preset threshold value, acquiring an image; calculating a definition index of the image; determining whether the image is a blurred image according to a preset definition standard and the definition index of the image; if the image is judged to be a blurred image, dividing the blurred image into a motion blurred image and a focusing blurred image; respectively constructing point spread function models of the motion blurred image and the focusing blurred image; performing deconvolution processing or depth reconstruction on the blurred image through a point spread function model to obtain a clear image; and recalculating the definition index of the clear image, and if the definition index does not exceed the definition threshold, triggering reacquisition or switching the repair model to execute secondary repair.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention discloses a cross-modal interactive image restoration method fusing text semantic guidance and visual structure priori, which comprises the following steps of: firstly, acquiring natural language description input by a user and an image to be restored, and generating a semantic segmentation map of the image through a semantic segmentation model; encoding the text and image semantics by using a pre-trained cross-modal encoding model to obtain text and semantic features; guiding a semantic alignment attention module through Prompt to realize deep fusion of multi-modal semantic features and image space features; structural enhancement and regulation of image features are realized by constructing a text guide weight graph, performing element-level modulation on the text guide weight graph and the optimized semantic segmentation graph, constructing a cross-modal structure semantic feature graph and generating a structural modulation factor; a four-stage image restoration network is adopted, and a high-quality restoration image conforming to semantic guidance and structure prior is generated step by step. According to the method, the semantic consistency, the structural integrity and the visual reality sense of an image restoration result are improved.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention discloses an automobile part defect detection method and system, and belongs to the technical field of image recognition, and the method comprises the steps: deploying at least one collection device at a detection line, transmitting first image data collected by the collection device to a data processing center, and generating second image data; establishing an illumination model, performing illumination analysis on the second image data, and identifying a first region and a second region; after the overexposure and shadow areas are identified, illumination correction and optimization are carried out on the image, and original details of the image are recovered; and performing quality evaluation on the repaired image, quantifying the image repairing effect by adopting a peak signal-to-noise ratio and a structural similarity index, and performing defect analysis after the evaluation is completed. In the implementation process of the technical scheme of the invention, by establishing the illumination model, performing illumination analysis on the second image data, and identifying an overexposure region and a shadow region, the image repairing efficiency is improved. And then different optimization methods are adopted for different areas, so that the image quality and the detection precision are improved.

The invention relates to the technical field of image processing, in particular to an integrated image restoration method and system based on image mask modeling, and the method comprises the following steps: collecting the boundary data of the whole image, separating the boundary of a mask region from the boundary of a non-mask region, and analyzing the gradient change characteristics of the boundary of the mask region. And extracting local depth information of the mask region from the gradient change features, and constructing a mask region boundary residual distribution model. According to the method, the mask region and the non-mask region are separated, the boundary gradient features are extracted, the residual distribution model is established on the basis of gradient change, the subtle change of the boundary of the mask region is accurately captured, and the accuracy of the boundary features is improved. By constructing a depth information data set and combining local depth information with gradient features, adjustment of layered dynamic weight distribution of a mask area basic structure is realized, effective extension of area features to the inside is ensured, and the possibility of boundary information loss is reduced.

The invention discloses a semantic-guided extreme overexposure image restoration method. The method comprises the following steps: 1, obtaining a data set, and dividing the data set into a training set and a test set; 2, extracting brightness information of the overexposure image, and generating a brightness map; 3, constructing an overexposure image brightness correction network model; 4, training an overexposure image brightness correction network model; 5, correcting the overexposure image by using the trained model, and generating a corrected image and a mask of a brightness saturation region; 6, constructing a pre-trained brightness saturation completion network model, and performing information completion by using the corrected image, semantic graph and brightness cue word; and 7, fusing the complemented brightness saturation region with the unsaturated region to obtain a final output image. According to the method, semantic information is applied to an image restoration process, restoration is effectively guided and constrained, and a visual pleasure and semantic coherence result is generated.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention provides a multi-degraded image restoration method based on self-adaptive prompt. The method comprises a two-stage hidden space diffusion model, and various degraded images can be flexibly processed. Specifically, the model generates a degradation cue by learning the difference between a degraded image and a clean image, and encodes the cue information in a hidden space. In the first stage, a degradation prompt is obtained through a degradation adaptive prompt learning mechanism as a key feature for guiding the diffusion model in the second stage to carry out image restoration. Finally, prompt information is integrated into a Fourier transform restoration network, and accurate restoration of different types of degraded images can be realized. The method not only improves the accuracy of image restoration, but also significantly reduces the calculation complexity, and is suitable for image restoration tasks in various real world scenes, including denoising, deblurring, defogging, rain removing, snow removing, raindrop removing and low illumination enhancement.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.

The present invention discloses a rapid construction method and system for a wireless channel knowledge map. The present invention uses image restoration technology to construct a channel knowledge map, and, by means of recovering a corresponding pixel value in an image matrix, predicts channel knowledge of a specific position, so that channel related knowledge of a user equipment at any position in a target area can be acquired. By means of combining the similarity of an image restoration problem and a channel knowledge map construction problem, and using a Laplacian pyramid frequency band decomposition framework, an input environment map can be decomposed at different frequencies. According to the characteristics of different frequency components, a corresponding sub-network is designed for feature extraction, and finally, the channel knowledge map is reconstructed by means of inverse operation of the Laplacian pyramid. The wireless channel knowledge map construction method provided by the present invention not only implements higher construction precision at lower computational complexity, but also has strong generalization ability for various wireless communication scenarios.

The present application relates to the technical field of image processing, and provides an image restoration method and apparatus, an electronic device and a storage medium. The method comprises: obtaining an image to be restored, and inputting the image to be restored into an image restoration network, wherein the image restoration network comprises a restoration stage and a generation stage; in the restoration stage, removing degradation in the image to be restored, so as to obtain a restored image; and in the generation stage, performing image detail supplementation on the restored image to obtain a target image. The present application solves the problem in image restoration related art that an image restoration method has insufficient generalization and poor robustness because the image restoration method cannot simultaneously realize image restoration on any image containing any degradation.

This document relates to providing adaptive teleconferencing experiences using generative image models. For example, the disclosed implementations can employ inpainting and / or image-to-image restyling modes of a generative image model to generate images for a teleconference. The images can be generated based on prompts relating to the teleconference. Users can be superimposed on the generated images, thus giving the appearance that the users are present in an environment generated by the generative image model.

The invention relates to the technical field of computer vision and artificial intelligence, in particular to an image restoration method based on automatic evaluation and dynamic optimization and related equipment. The method comprises the following steps: acquiring a to-be-restored image and a corresponding restoration area; performing restoration processing on the to-be-restored image to generate an initial restoration result; performing automatic quality evaluation on the initial repair result by using a multi-modal large model in combination with an anomaly detection rule base to obtain an evaluation result; based on the evaluation result, if it is judged that the repair quality does not reach the standard, an intelligent adjustment tool chain is called to carry out targeted optimization on the initial repair result, and an optimized repair result is generated; and repeatedly executing the quality evaluation and optimization steps on the optimized repair result until the repair quality reaches the standard, and forming a closed-loop processing flow. The method has the effects of improving the restoration quality in a complex scene in an image restoration technology, improving the image restoration efficiency and increasing an automatic closed-loop processing mechanism.

Disclosed are an image restoration method and device, and a non-transitory computer storage medium. The image restoration method includes: an original mask in an original to-be-restored image is processed into a dilated mask with a regular shape, the dilated mask is overlaid at the location of the original mask in the original to-be-restored image to acquire a mask enhancement feature map, and then the dilated mask in the mask enhancement feature map is restored using an image restoration network to acquire a restored image corresponding to the original to-be-restored image.

The invention discloses an out-of-focus identification and restoration method for a microscopic system. The method comprises the following steps: 1, preparing a microscopic out-of-focus image data set and carrying out degradation modeling; 2, constructing an out-of-focus parameter prediction network based on multi-label parameter reasoning and microscopic system priori knowledge and an image restoration network based on a generative adversarial network, performing independent training, and then performing merging training through a circulation system formed by mutual connection based on predicted point spread function convolution and deconvolution operation; 3, model fine tuning based on a specific system and a new data set; and 4, performing model testing, performing large-view image sliding window detection and collage fusion, and explicitly outputting defocus parameters of the system during image shooting. The method can be applied to out-of-focus fuzzy recognition and restoration of microscopic imaging of various systems, and is beneficial to the accuracy of functions such as particle counting and particle size statistics of the systems, thereby further promoting the application of the full-depth-of-view microscopic system in biomedical detection.

The invention discloses an image restoration method based on a gated Fourier convolution residual error and a multi-head attention mechanism, and the method is characterized in that the method specifically comprises the steps: collecting a damaged image as a data set, carrying out the preprocessing, and dividing the data set into a training set and a test set according to a set proportion; constructing an LGFCDANet network, wherein the network comprises a coarse recovery network and a fine recovery network; carrying out adversarial training on the LGFCDANet network to obtain a trained LGFCDANet network, and testing the trained LGFCDANet network by adopting a test set; and carrying out image restoration by adopting the trained network. The invention aims to enhance the understanding and recovery capability of the model on image details, thereby improving the restoration effect and enhancing the dynamic adjustment capability on the color and texture of a special area.

The embodiment of the invention provides an image restoration method and device, electronic equipment and a storage medium, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring a target detection frame and a redrawn mask of a to-be-restored image; cutting the to-be-restored image according to the target detection frame to obtain a local image; generating a guide vector according to the local image; inputting the guide vector and the redrawn mask into a diffusion model to obtain a target feature; and inputting the target feature into an image decoder for decoding to obtain a redrawing image. According to the embodiment of the invention, the distorted hand region in the portrait can be effectively repaired, the image quality is improved, the style consistency of the repair result is kept, and the repair effect is improved.

The invention discloses an image restoration method based on a multi-scale residual module and feature fusion, and belongs to the technical field of image processing. The multi-scale residual module designed by the invention can effectively capture feature information of different scales of the image, the quality of the repaired image is improved from a fine texture to a macroscopic structure, a feature fusion strategy fusing detail branches and semantic branches can fully integrate features of different levels, the detail expression and overall coordination of the image are enhanced, and the image quality is improved. Through fusion of multi-scale features, limitation possibly brought by single-scale features is avoided, and the repaired image is more real and fine in color, texture and structure. And the multi-scale residual error module can relieve the gradient disappearance problem, the calculation efficiency is high, the image restoration task can be completed in a short time, and the introduced and designed dual-channel global attention module has good robustness, so that the method can stably play a role in various complex image damage conditions.

The embodiment of the invention provides an image generation method, an image generation device, electronic equipment and a storage medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring an image generation prompt word and carrying out style understanding according to the image generation prompt word to obtain a scene style feature; performing scene expansion on the image generation prompt word according to the scene style feature to obtain a scene expansion word; performing scene arrangement according to the scene expansion word to obtain scene features; performing information fusion according to the scene style features, the scene expanding words and the scene features to obtain scene description information; performing image generation according to the scene description information to obtain a preliminary image; and performing image restoration according to the initial image and the scene description information to obtain a target image. The image generation method and device can be applied to business systems needing a large number, such as financial science and technology and medical health care, and the image generation efficiency and precision can be improved.

A model training method includes: acquiring a plurality of image sample pairs, where the image sample pair includes a first image sample and a second image sample of a same image, and image quality of the second image sample is higher than image quality of the first image sample; and training a first preset model with the plurality of image sample pairs as training samples, where the first preset model is configured to improve the image quality of the first image sample, and a process of the training includes: acquiring text data corresponding to a prediction image currently output by the first preset model, where the text data includes data for evaluating image quality of the prediction image; and updating parameters of the first preset model based on the text data, the prediction image, and the second image sample.

The invention provides a face image restoration method based on generative adversarial network inverse mapping, and belongs to the technical field of image restoration and computer vision. According to the method, an encoder-decoder architecture is adopted, an encoder is a ResNet combined with a channel attention mechanism and can encode a to-be-restored face image (containing block-shaped shielding) to a potential space of a StyleGAN, and a W + vector of 18 * 512 dimensions is obtained; and the decoder is a pre-trained StyleGAN generator, and can convert the potential vector into a complete face image to realize restoration. In the training process, through a weighted combination constraint model of L2 loss, perception loss and face identity loss, it is ensured that a restoration result is consistent with an original image in pixel, feature and identity levels. Meanwhile, by means of the decoupling characteristic of the StyleGAN potential space, the repaired face features (such as smile and age) can be edited. According to the method, complex preprocessing is not needed, large-area missing face images can be efficiently repaired, identity consistency is kept, and the method is suitable for monitoring image enhancement, old photo repair and other scenes.

The invention provides an image restoration and generation method based on a conditional generative adversarial network, and the method comprises the steps: a camera APP obtains contour key point data of a user figure proportion through carrying out the high-resolution feature extraction of an input image, carries out the three-dimensional reconstruction of a waist line and a shoulder breadth size, and obtains a contour key point data of a user figure proportion; generating an initial feature mapping graph containing wrinkle density distribution and local deformation amplitude; according to the initial feature mapping graph, adopting a region segmentation technology to separate a body contour from a clothing region, extracting a texture stretching degree and contact point stress distribution, determining a distribution condition of clothing and body contact points, and generating a segmentation result for dynamic adjustment; and for the to-be-optimized detail area list, performing enhancement processing on the texture stretching degree of the contact points of the clothes and the body and the stress distribution of the contact points, obtaining wrinkle layer depth data of a local area, and generating an optimized image layer.

The embodiment of the invention discloses a video generation method and device, electronic equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: generating a rough three-dimensional world of a target scene formed by a three-dimensional Gaussian ball set, and automatically searching a multi-angle offset view angle at the same coordinate position as a new view angle. According to the method, a multi-view-angle combined repairing mechanism is realized, geometric and semantic consistency of image data among different view angles is ensured, consistency of scene structures, textures and semantic information at different observation angles is realized, and a continuous, coherent and high-quality three-dimensional world is constructed. According to the method, the problem of multi-view inconsistency in a traditional method is solved, the new view image quality under large view conversion is improved, the image quality problems of floating objects, artifacts, structural distortion and the like in the image are eliminated, the generated image is more stable and real, the spatial continuity is improved, and the immersion and exploration experience of a user are enhanced.

The invention discloses a three-dimensional scene generation method based on a diffusion model and Gaussian splashing and related equipment, and belongs to the field of computer vision. The method comprises a point cloud construction stage and a three-dimensional Gaussian optimization stage: in the point cloud construction stage, extracting an initial point cloud from an input initial image, and expanding the point cloud through a Stable Diffusion image restoration model, a monocular depth estimation model and a boundary perception depth alignment module to create a scene; in the three-dimensional Gaussian optimization stage, three-dimensional Gaussian is initialized according to the obtained point cloud, the three-dimensional Gaussian is utilized to represent the whole scene, a multi-view image is adopted as a supervision signal to optimize parameters of the three-dimensional Gaussian, and a three-dimensional scene capable of being browsed in an immersive mode is obtained after optimization. According to the method, the high-fidelity three-dimensional scene with accurate geometry and natural appearance can be generated without training any three-dimensional data, the problem of dependence on three-dimensional scene data is solved, the requirement for computing power resources is small, and the manufacturing cost of the three-dimensional scene is reduced.

The invention provides a blind person face image restoration method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a to-be-restored image of a blind person face; inputting the to-be-restored image into the image restoration model to obtain a target restoration image output by the image restoration model; wherein the image restoration model comprises a degradation prediction network, a text prompt module, a visual prompt module and a decoding module; according to the method, a degradation prediction network can effectively evaluate the degradation degree of an input to-be-restored image and provide a corresponding degradation probability weight so as to enhance the prompt generation capability of a subsequent task; moreover, the introduction of the degradation prediction network improves the adaptability of the image restoration model, and enables the model to adjust a prompt strategy according to the degradation degree, thereby optimizing the final image restoration effect of the face of the blind person, and improving the fidelity of image restoration.

The invention discloses an image restoration method and system based on gated convolution and a fused gated attention mechanism, and belongs to the field of image super-resolution restoration frameworks, and the method comprises the steps: taking a preprocessed image as the input of an image restoration model, carrying out the feature extraction and dimension raising, and obtaining a feature map; the image features are extracted by fusing the attention and self-attention mechanism of the convolutional neural network type, and iterative optimization is carried out by using the loss function, so that the local and global features of the image can be processed more effectively, the image reconstruction quality is improved, and the authenticity and detail richness of the high-resolution image are ensured at the same time.

The invention provides an image generation method and system based on AI identification. The method comprises the following steps: S1, obtaining text data input by a user; s2, slicing the text data content to obtain a plurality of slice contents; s3, performing identification and target prediction on the plurality of slice contents by using a Transform model; s4, constructing an image generation model and training; s5, converting the plurality of target prediction results in the step S3 into image feature vectors, and inputting the image feature vectors into an image generation model to generate a plurality of initial images; s6, fusing the plurality of initial images through an average value fusion mode; and S7, performing image restoration on the fused image to obtain a final image, converting the predicted target object and the attributes thereof into the image feature vectors, inputting the image feature vectors into the image generation model to generate a plurality of different initial images, and merging the plurality of initial images into a comprehensive image by adopting an average value fusion mode, so as to obtain the final image. The quality and consistency of images are improved, and the shielding phenomenon is avoided.