624 results about "Inpainting" patented technology

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 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 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.

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 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 relates to the technical field of image processing, in particular to a lightweight multi-focus image fusion method and electronic equipment based on image restoration and SwinFusion, and the method comprises the following steps: S1, building a data set; s2, performing preliminary fusion; s3, repairing details; s4, feedback training; according to the method, the Swin Transform self-attention in the image fusion network is replaced by the dynamic sparse attention, the same and even higher model efficiency can be realized with lower resource consumption, and the method is suitable for resource limited scenes and high-resolution complex tasks; according to the invention, an image restoration task is added in the multi-crossing image fusion, so that missing focusing and non-focusing areas in a source image can be restored; through mutual guidance between the fusion network and the repair network, all focusing contents can be fused into the fusion image, so that the overall definition and details of the fusion image are improved, and the method has a wide application prospect.

The invention belongs to the technical field of rice panicle shielding image data processing, and provides a rice panicle shielding image restoration method and system based on a generative adversarial network, and the method comprises the steps: collecting rice panicle images at different angles and under different illumination conditions to construct a data set, employing a target detection model to carry out the positioning of a rice panicle region, and classifying the shielding types, and extracting a visible area of the rice spike by adopting a semantic segmentation network, and repairing a sheltered area under a generative adversarial network framework to obtain a complete rice spike image. The method is suitable for complex scenes such as leaf shielding, inter-panicle mutual shielding and mixed shielding, texture details and structure consistency of the repaired image can be guaranteed, acquisition of complete phenotype information of the rice panicles is achieved, and reliable data support is provided for rice yield estimation and precision agricultural management.

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

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

The invention discloses a chapter-level Chinese ancient book image restoration method and related equipment, and belongs to the field of ancient book restoration. The method comprises the following steps: acquiring ancient book images, and screening out intact ancient book images and damaged ancient book images; for an intact ancient book image, simulating damage in a random mask mode, and constructing a training data pair; for the damaged ancient book image, marking the position of the incomplete character, and determining the specific content of the incomplete character; constructing a repair model based on the diffusion model, and training the repair model; and using the trained model to gradually realize the restoration of the chapter-level ancient book image in the form of an overlapped sliding window in an autoregression mode. According to the method, on the basis of the damaged positions and the damaged text content of the ancient books marked by human experts, the diffusion model technology is adopted, an autoregressive restoration method is combined, automatic restoration of the chapter-level damaged ancient book image is achieved, and the overall consistency of the font style and the background is effectively kept in the restoration process.

The invention provides an image restoration method based on adaptive weighted tensor completion, and relates to the technical field of image processing and application, and the method comprises the steps: obtaining to-be-restored image data, and carrying out the tensor of the to-be-restored image data, and obtaining input tensor data; constructing a tensor completion model based on an adaptive weighted tensor nuclear norm; wherein a weight matrix in the tensor completion model can be adaptively updated along with input tensor data; and based on an alternating direction multiplier method or an approximate singular value decomposition method based on tensor QR decomposition, solving the tensor completion model, and outputting restored tensor data to realize image restoration. According to the scheme, the image restoration quality can be improved.

The application discloses a kind of detection and defense method of counterpatch, respectively based on abnormal positioning and edge detection.The detection method of counterpatch based on abnormal positioning utilizes clean image to train the generator-adversary network of encoder-decoder structure;The output image is obtained by inputting the image to be detected into the generator-adversary network, and the absolute error is obtained by subtracting and taking absolute value;The image region whose absolute error is greater than error threshold is the region where counterpatch is located;The detection method of counterpatch based on edge detection converts the image to be detected into gray scale image and carries out edge detection, and obtains edge image;The edge lines in edge image are connected into a closed area one by one, and the closed area whose area is less than the area of counterpatch region is the region where counterpatch is located.The application can detect counterpatch based on the two schemes of abnormal positioning and edge detection respectively, and blacken the region or use image restoration algorithm to restore the region to defend counterpatch.

The invention provides an image restoration model training method, an image restoration method and related equipment, and relates to the technical field of image processing. The training method is applied to a cloud server, and comprises the following steps: acquiring a training sample set comprising a plurality of high-definition sample images; performing compression processing on the high-definition sample image to obtain a corresponding sample compression image; determining a sample residual image according to the high-definition sample image and the sample compressed image; based on an image restoration model, performing restoration processing on the sample compressed image according to the sample residual image to obtain a prediction restoration image; determining target loss according to the prediction restoration image, the high-definition sample image and the sample residual image; and training an image restoration model according to the target loss to obtain a trained image restoration model. The image restoration model trained through the method can effectively restore the quality degradation problem caused by loss of compression information, so that the restoration precision and the playing image quality can be improved when the terminal equipment deployment model carries out image restoration.

The invention relates to a document image restoration method and system, and the method comprises the steps: obtaining a to-be-restored document image, carrying out the preliminary reconstruction through a pre-trained preliminary restoration network, and obtaining a preliminary restoration image; document structured information of the to-be-repaired document graph is extracted, and text features are obtained based on the document structured information; performing text content coding, position information coding and confidence coefficient coding on each text feature, and performing fusion through a feature fusion module to generate a text control feature; inputting the text control feature and the preliminary restoration image into a ControlNet together to generate a control signal; and inputting the control signal into a pre-trained potential diffusion model to de-noise the potential spatial features step by step, and decoding the de-noised potential spatial features by using a pre-trained potential decoder to generate a repaired document image. The method and the device have the effect of improving the document image restoration efficiency and the restoration effect.

The invention discloses a unified underlying vision pre-training method based on a multi-scale diffusion model, and the method comprises the steps: constructing and training a multi-scale degradation robust variational auto-encoder (VAE) which is used for extracting the multi-scale hidden space representation of degradation robustness; training a degradation invariant image feature encoder for extracting visual semantic features irrelevant to degradation types; based on a pre-trained diffusion model backbone network, in combination with the robust hidden space representation and the visual semantic features, constructing and training a condition-controllable bottom layer visual diffusion model, and modeling a diffusion process by adopting an improved stochastic differential equation (SDE); and integrating the multi-scale degradation robust VAE with a bottom-layer visual diffusion model, and constructing a large multi-scale bottom-layer visual pre-training model. According to the method, unified pre-training of various underlying degeneration is realized, and the fidelity and robustness of image restoration are improved.

The invention discloses a method for optimizing the texture quality of a generative 3D model based on a texture expansion graph, and relates to the field of computer vision. Rendering the 3D model to obtain a multi-modal image set under multiple view angles; obtaining a training data set and training a multi-view generation model; generating an RGB image set by using a multi-view generation model; high-quality RGB images under the six visual angles are back-projected to the surface of the 3D model, colors of 3D points are mapped to a UV space through UV mapping, and a multi-visual-angle texture map and a mask map of the UV space under the six visual angles are obtained; fusing the multi-view texture maps to obtain a rough texture map; and optimizing the rough texture map through the image restoration model to obtain a high-quality complete texture expansion image. Through the multi-view generation model and the UV space image restoration technology, the texture quality of the generation type 3D model is optimized, and the problems of artifacts and distortion caused by the fact that the image content is inconsistent with the three-dimensional model geometry when high-quality multi-views are generated based on images are effectively solved.

The invention relates to a Tibetan Thangka image restoration method and system based on a GAN network. The method comprises the following steps: constructing a Tibetan Thangka image sample data set; inputting the Tibetan Thangka image sample data set into a generator of the OA-GAN network; in the generator, mineral pigment features are extracted through a self-adaptive channel attention mechanism, and line features are extracted through a dynamic space attention mechanism; generating a Tibetan Thangka restoration image according to the extracted mineral pigment features and the extracted line features; inputting the Tibetan thangka restored image into a discriminator of the OA-GAN network; in the discriminator, constructing a mixed loss combination function through an L1 reconstruction loss function, a color smooth loss function, a perception loss function and an adversarial loss function, and discriminating the Tibetan Thangka restored image through the mixed loss combination function; and confrontation training is carried out according to a discrimination result of the discriminator, a trained image restoration model is obtained, and the image restoration model is used for outputting a restored Tibetan Thangka image when the to-be-restored Tibetan Thangka image is input.

The embodiment of the invention provides an image restoration method and device based on a diffusion model and generative adversarial training, and the method comprises the steps: obtaining image data, inputting the image data into a degeneration encoder, and obtaining a first result; providing an interaction page, and obtaining image limitation information based on the interaction page; the first result and the image limiting information are input into an image restoration network to obtain a second result, and the image restoration network is generated based on a diffusion model in a pre-trained diffusion network; inputting the second result into an image decoder for processing to obtain a restoration result, and feeding back the restoration result; according to the scheme, the pre-trained encoder and the diffusion model can be finely adjusted so as to be suitable for restoration of the degraded image, and a more accurate restored image can be obtained.

The invention discloses an image restoration method and system coping with various severe weather environment factors, and relates to the technical field of image restoration, and the method comprises the steps: recognizing an image weather type, and carrying out the test time self-adaption of a model; inputting an image into the adaptive model to obtain a feature vector of the image, and decoding the feature vector through a decoder; and transmitting the image to a soft reconstruction layer to obtain a restored image. According to the invention, a supervised learning mode is adopted to train a universal network capable of coping with multiple weathers. A soft prompt excitation model is introduced in the fine tuning stage to enhance the generalization ability to cope with different weathers. Explicit and implicit interaction is utilized to enhance the prompt effect, hidden information of prompts is explored through low-rank decomposition, and by adding contrast loss, prompts approach to similar tasks and leave away from opposite tasks, and the feature coding capacity is improved. According to the method, the influence of various severe weathers on the image can be effectively processed, the image quality is improved, and high-quality input is provided for subsequent defect identification and auxiliary analysis.