285 results about "Restoration method" patented technology

The present application discloses a face image restoration method based on a state space model, which includes inputting the to-be-restored face image to a restoration model to obtain a restored face image; the restoration model includes an encoder and a decoder; the encoder sequentially includes a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module, and a third multi-scale state space module; the decoder sequentially includes a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module, and a third multi-scale attention fusion module. This method ensures the consistency of face semantic information while restoring the detailed texture, especially low quality face images in real degraded scenes can achieve better restoration results.

The invention provides a chemical restoration method for treating heavy metal-polluted terrain soil by ferromagnetic artificial nucleuses. In a ferromagnetic artificial nucleus soil restoration method, the ferromagnetic artificial nucleuses are applied to heavy metal-polluted soil; soil above a heavy metal pollution depth is overturned by cultivation equipment to uniformly mix with the ferromagnetic artificial nucleuses; and after heavy metal can be dispersed from the soil to the artificial nucleuses by a period of time, the soil containing the artificial nucleuses is overturned by using the cultivation equipment to increase the change of the artificial nucleuses contacting with other soil. After the soil overturning procedure is repeated by multiple times, the cultivation equipment provided with magnets can be used for separating the ferromagnetic artificial nucleuses, absorbed with the heavy metal, from the polluted soil; and the content of the heavy metal of the polluted soil is reduced due to shifting-out of the ferromagnetic artificial nucleuses, so that the purpose of soil restoration is gradually achieved.

The invention discloses a diffusion model character restoration method and device based on dual-condition guidance, and belongs to the field of artificial intelligence of character restoration technology, and the method comprises the steps: obtaining a first feature matrix and a second feature matrix; taking the first feature matrix as a query vector, and carrying out cross-attention fusion on the query vector and a key vector and a value vector of the second feature matrix to generate a target condition vector; injecting the target condition vector into a noise preset network of a diffusion model of which the initial input is the to-be-repaired character image, and dynamically fusing the local feature of the jump connection with the target condition vector through a cross attention block to generate an enhanced feature; and generating a repaired character image by combining weight scheduling of skeleton priori and stroke priori. According to the method, the structure and stroke features are obtained through the two feature matrixes, and character constraint conditions are dynamically introduced in the reverse denoising process of the diffusion model, so that the model can retain details of strokes and character style features while generating a high-quality repair result.

The invention discloses an ancient fresco restoration method based on progressive reconstruction and damage perception self-adaption, and relates to the technical field of computer vision and image processing, a damaged fresco image is input into a coarse restoration network for initial restoration, in the initial restoration process, a discriminator and the coarse restoration network are adopted for adversarial training, and a damaged fresco image is obtained; generating an initial restoration result of the mural image; inputting the initial repair result and the mask image into a mask-guided local information extraction network to obtain a local optimization result; and inputting the local optimization result into a global information extraction network to obtain an overall restoration result of the mural image. The problem that an existing network is low in efficiency in the aspect of capturing local details and global styles is solved. The method has good applicability to damaged wall paintings; the problem of fuzzy texture in the repairing result is effectively solved; local features are adaptively extracted and fused according to the damage degree of the mural, and the multi-stage residual information distillation module further refines details of the mural on different scales.

The invention belongs to the technical field of computer vision three-dimensional scene repair, and provides an automatic driving three-dimensional scene repair method based on color point cloud prior guidance, which comprises the following steps: acquiring multi-source heterogeneous data; constructing an incomplete semantic two-dimensional Gaussian field according to the multi-source heterogeneous data, and generating an incomplete image sequence, an incomplete depth image sequence and an opacity image sequence; obtaining a color point cloud based on the instance segmentation image, the opacity image, the incomplete image and the incomplete depth image of the reference image; generating a texture pseudo view angle sequence based on the color point cloud; generating a repaired image sequence by taking the texture pseudo view angle sequence as a condition signal of the fine-tuning video diffusion model; generating a repair depth sequence based on the color point cloud; calculating a repair Gaussian loss function based on the repair image sequence and the repair depth sequence, and performing iterative repair optimization on the incomplete semantic two-dimensional Gaussian field to obtain a complete repair two-dimensional Gaussian field; the invention further discloses a computer device. And texture-geometry collaborative automatic driving scene three-dimensional repair is realized.

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 belongs to the technical field of cultural relic virtual restoration, and discloses a cultural relic virtual restoration method and device, equipment and a storage medium. The method comprises the following steps: acquiring text information and image information of a to-be-restored cultural relic; analyzing the text information and the image information; the analyzed key information is fused, and a repairing instruction is generated; based on the restoration control instruction, generating a restored cultural relic image through a diffusion model; performing dynamic parameter optimization on the restored cultural relic image; and decoding the optimized image data into a visual image and outputting the visual image. Through the above mode, accurate capture of the damaged area of the cultural relic and generation of the personalized restoration scheme are realized, secondary damage possibly caused by a traditional restoration method to the cultural relic is avoided, the text information of the cultural relic is fused on the basis of the image, the original appearance and safety of the cultural relic are ensured, and the cultural relic after virtual restoration better conforms to the real situation.

The invention relates to the technical field of image restoration, in particular to a spatial frequency hybrid restoration method for JPEG (Joint Photographic Experts Group) compressed blurred image restoration, which is characterized in that a spatial frequency hybrid restoration network is constructed for compressed blurred image restoration and comprises three coding blocks and three decoding blocks, each of the coding block and the decoding block is composed of a plurality of spatial frequency mixing blocks; the spatial frequency mixing block comprises a spatial frequency double-branch mixing structure and an ISS block; the SFHRN provided by the invention can realize a more efficient restoration effect on the compressed blurred image with lower model parameter quantity and calculation complexity.

The invention discloses a video restoration data set construction and restoration method combined with generation of a large model, and belongs to the technical field of video restoration, and the method comprises the following steps: S1, slicing; s2, screening: performing aesthetic scoring and motion detection on the slices, and screening and retaining high-dynamic and high-image-quality fragments; s3, segmenting a core object; s4, extracting text features; s5, model input; s6, performing feature fusion; s7, model training: only training parameters of the cross attention layer, and locking gradients of other layers to reduce the calculation cost; s8, calculating a loss function; s9, video input: inputting a to-be-restored video, and generating an object Mask and text description of the to-be-restored video; and S10, outputting the restored video: inputting the Mask video, the object Mask and the text description into the improved generated large model, and outputting the restored video. According to the staged feature injection strategy, the global naturalness and the local reality sense are considered, and the visual consistency of the repaired content and the original video is remarkably improved.

The invention discloses a historical document restoration method and system based on implicit interpolation network enhancement. The method comprises the following steps: acquiring a damaged historical document image, a position mask image for identifying a to-be-restored area and a content guide image for guiding the semantic meaning of restored content; constructing a historical document repair model based on a de-noising diffusion probability model; training the historical document repair model by using a training data set containing damaged image and intact image pairs; and inputting the damaged historical document image to be repaired, the position mask image and the content guide image into the historical document repair model, and outputting the repaired historical document image. According to the method, the accuracy of the character content in the restoration area and the naturalness of the visual boundary are remarkably improved, and high-fidelity digital restoration of complex degraded literatures such as ancient books and inscription rubbings is realized.

The invention discloses a document image illumination recovery method and system based on a diffusion model, and the method comprises the following steps: constructing a conditional diffusion module which takes a shadow image as conditional input, inputs a noise image, and predicts a conditional scoring function; a document image illumination recovery module is designed, a coarse-to-fine processing strategy is adopted, a preliminary degradation map is predicted through a coarse network, and more accurate degradation mapping is predicted through a fine network in combination with a shadow image, a scoring function and preliminary degradation mapping; applying the accurate degradation mapping to the shadow image to realize illumination recovery of the document image; a high-resolution complex illumination recovery test set with rich layout is constructed, and the generalization ability of the model is evaluated. According to the method and the device, effective recovery of the illumination of the document image is realized, meanwhile, the efficiency of processing the high-resolution image is improved, and the problems of poor recovery effect, low processing efficiency and inconsistent generation results when the illumination of the document image is recovered in the prior art are solved.

The invention discloses a lightweight mural virtual restoration method based on an inactive generative model, and belongs to the technical field of digital image processing and computer vision. The objective of the invention is to solve the technical problem of semantic and texture recovery imbalance of an existing deep learning repair model in face of cultural heritage such as murals. A damaged image and a mask are stacked into a multi-channel tensor to be input into a pre-trained AFMNet model, the model adopts an encoder-bottleneck-decoder architecture, and an encoder uses a multi-domain partial convolution module without an activation function to synchronously extract and fuse local texture and frequency domain global structure information and synchronously update the mask; the bottleneck portion models the lowest resolution features and masks thereof through a lightweight multi-head attention module to capture remote dependencies, and a decoder upsamples in conjunction with the features of the jump connection transfer to reconstruct image details. The method is mainly used for carrying out efficient and high-fidelity digital virtual restoration on damaged visual cultural heritage such as ancient murals.

The invention discloses an all-in-one method for removing severe weather by fusing a two-stage converter with a double-time-phase feature, and relates to the technical field of artificial intelligence image processing and computer vision. Compared with a traditional image restoration method, the method does not need to manually provide priori knowledge, and enables the model to have powerful performance and more accurate restoration results in the restoration task of the weather degradation image by means of the powerful learning capability of the neural network; compared with other deep learning image restoration methods, the method has the advantages that reasonable design and expansion are performed on the basis of Transformer, the proposed FDFormer network can utilize channel and space two-stage attention to enable the model to capture feature information more comprehensively and enhance details, robustness of the model to different weather and different data sets is enhanced, and the method is suitable for being applied to deep learning image restoration. Compared with other excellent weather degradation image restoration methods, the method has better performance and generalization ability; and the method also has good performance for challenges such as unlearned severe environments and the like.

The invention discloses an urban shallow lake ecological protection and restoration method and system integrated with intelligent management and control, and relates to the technical field of water environment ecological management and intelligent water affairs, and the method comprises the steps: constructing a three-dimensional monitoring network to synchronously collect water quality, image and meteorological data, and carrying out the fusion to generate a multi-modal data set; a deep learning model is utilized to realize pollution source tracing, water quality prediction and algae bloom early warning in parallel; based on the prediction result, outputting a multi-facility cooperative regulation strategy through a reinforcement learning agent; and converting the strategy into a hierarchical instruction to drive an execution unit, and feeding back the treated environment state to the model and the intelligent agent to form closed-loop optimization. According to the invention, whole-course intelligent management and control from monitoring to execution are realized, systematicness, accuracy and perspectiveness of lake treatment are effectively improved, and water environment risk response capability and ecological restoration effect are significantly enhanced.

The invention relates to a compression method, a reduction method and a processing device for template files, and the compression method comprises the steps: carrying out decompression processing on a to-be-compressed target document, and obtaining an object folder in a preset format; performing decompression processing on a template document of the target document to obtain a template folder in the preset format; the target document and the template document have common elements; and generating a compressed file of the target document according to difference information between the object folder and the template folder. According to the compression method, a large amount of storage space is saved, and the document compression rate of a single document and batch documents is effectively improved. The reduction process consumes very short time, and a single document or batch documents can be efficiently executed.

The invention discloses a mural restoration method and system based on a pulse coupling neural network and a diffusion model, and relates to the technical field of mural restoration. The method comprises the following steps of: firstly, enhancing an image by adopting a three-dimensional pulse coupled neural network enhancement module (SMSPCNN), removing noise and reserving original features; then global and local features are fused through the diffusion reconstruction module, the extraction module is responsible for extracting corresponding features, and finally the attention feature fusion module is used for fusing the features and enhancing expression, so that local and global information fusion is realized, and the model performance is improved. According to the method, the damage of the Dunhuang mural can be effectively repaired, details and structures are reserved, the sense of reality is enhanced, multiple indexes in a quantitative contrast experiment are superior to those of other methods, and a new way is provided for digital repair of the Dunhuang mural.

The invention provides an attack path restoration method and equipment, which can be applied to the technical field of computers. The method comprises the following steps: monitoring the running state of an application program in each server by using an RASP agent installed on each server, and collecting key data of the application program in the running process; when an attack event is detected, alarm information is generated according to the key data, and a target server having an association relationship with the attack event in the servers is determined according to the alarm information; generating a server attack path according to the interaction relationship among the plurality of target servers; and generating an attack path corresponding to the attack event according to the server attack path and each component in the target server. According to the method, the attack path of the attack event can be quickly and accurately restored.

The invention discloses a goaf ecological restoration method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source remote sensing data and geological survey data of a goaf, and generating a three-dimensional geological damage model of the goaf through a space-time fusion technology; performing multi-scale feature extraction on the three-dimensional geological damage model by using a deep convolutional neural network to obtain a multi-dimensional ecological feature data set, identifying an ecologically fragile region and an instability risk region, and outputting an ecological risk level map; performing parallel processing on the multi-dimensional ecological characteristic data set to generate an ecological assessment report containing health degree scores of each ecological subsystem of the goaf; and based on the ecological risk grade map and the ecological assessment report, a multi-objective optimization algorithm is adopted to generate a vegetation-soil / rock mass-water body collaborative restoration strategy. By means of the goaf ecological restoration method and device, accurate assessment and collaborative strategy generation of goaf ecological restoration can be achieved, and scientificity, efficiency and overall ecological benefits of restoration engineering are improved.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention relates to the technical field of generative image restoration, in particular to a defect restoration method for a three-dimensional reconstruction model of a porous structure, which comprises the following steps: generating a basic three-dimensional model of a target object according to a target resolution; scanning a target object and determining a corresponding to-be-repaired area; according to the target depth of the to-be-repaired area, determining a corresponding repair focus and a repair resolution; generating a repair container, and placing the geometric center of the repair container on the repair focus; filling the repair container with the repair resolution according to a preset sequence, and generating a plurality of repair models; identifying feature points of each repair model and the basic three-dimensional model, and merging the repair models into the basic three-dimensional model according to the similarity of the feature points; according to the method, the corresponding repairing features are selected, the model for repairing is merged into the target model according to the features, and edge smoothing processing is performed, so that the integrity of the model with the holes is effectively improved while the quality of the model is improved.

This invention is a multi-dimensional ancient building maintenance and restoration method based on 3D laser scanning, relating to the field of computer processing technology. The method comprises the following steps: S01, using a terrestrial laser scanner to acquire basic 3D point cloud data of the ancient building. This basic 3D point cloud data includes geometric information of the building facade, ground structure, and near-ground decorative components; S02, using an unmanned aerial vehicle equipped with a multi-line laser scanner to scan the building roof and high-altitude structures to generate supplementary point cloud data. This supplementary point cloud data covers the millimeter-level precision 3D coordinates of the sloping roof, tower eaves, and eaves brackets; S03, using a multispectral imaging device to simultaneously collect surface texture data of the ancient building. This invention achieves millimeter-level accuracy in 3D reconstruction of ancient buildings through multi-source data fusion and a hierarchical optimization algorithm. Combined with automated defect identification and multimodal analysis techniques, this method improves the efficiency of maintenance decision-making.

The invention relates to a relics restoration method based on a meta-universe and a digital twinborn technology, and aims to solve the problems of relics disappearance and historical memory deficiency in urban construction, and utilizes the meta-universe and the digital twinborn technology to build a relics 3D model on a meta-universe platform to realize scene restoration. A virtual scene is constructed through software such as 3DMAX and Unity 3D and related technologies, and the characteristics of identity, social contact, immersion and the like are achieved; the method has obvious advantages, the cost can be reduced, the efficiency can be increased, the real scene can be stably simulated, and multi-place policy support can be obtained. The market potential is large, and the method faces college teachers and students, culture enthusiasts and other groups. Popularization strategies such as social media propaganda and SEO are adopted, and the product has unique advantages and innovation and is flexible in pricing; in addition, the method has both campus and social public welfare, provides an innovative scheme for relics recovery, and is of great significance in the fields of cultural inheritance, education and the like.

The invention discloses an electric imaging logging blank area intelligent restoration method based on graph adversarial transfer learning. The method comprises the steps that a first training data set is established according to public image data; constructing an initial image restoration model based on the graph neural network and the generative adversarial network, and training and testing the initial image restoration model by using the first training data set to obtain a pre-trained image restoration model; acquiring conductivity logging data, and establishing a second training data set according to the conductivity logging data; carrying out migration training on the pre-trained image restoration model by utilizing the second training data set to obtain an optimized image restoration model; and inputting a to-be-repaired electric imaging logging image into the optimized image repair model to complete the repair of the electric imaging logging image. The method can effectively improve the restoration precision of the image of the electric imaging logging blank area, fills up the image missing area caused by the instrument problem, and also has remarkable advantages in the aspects of processing speed and adaptability.

The embodiment of the invention discloses an audio acquisition real-time restoration method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring real-time audio data generated by audio acquisition; the real-time audio data is input into a tone quality restoration model obtained through pre-training, the input of the tone quality restoration model is a PQMF molecular band, the tone quality restoration model is obtained through training based on a generative adversarial network, the generative adversarial network comprises a generator and a discriminator, the generator comprises an encoding module and a decoding module, and the discriminator comprises an encoding module and a decoding module; the coding module comprises a causal depth separable convolution module and a grouping LSTM module, the decoding module comprises a mask path and a mapping path which both use causal depth separable transpose convolution, and feature fusion is carried out on output complex spectrums of the mask path and the mapping path and a PQMF molecular band; and taking the output of the sound quality restoration model corresponding to the real-time audio data as real-time restoration audio. According to the scheme, the time for repairing the audio to ideal quality can be shortened, and the speed requirement of audio processing in a real-time application scene is met.

The present application relates to a power distribution network restoration method and apparatus, and a device, a storage medium and a program product. The method comprises: by taking a target restoration amount that corresponds to a power distribution network to be restored in a preset restoration period being maximized as an objective, constructing an objective function for restoring said power distribution network; acquiring a constraint condition for constraining said power distribution network, wherein the constraint condition comprises a resource scheduling constraint condition for constraining the scheduling of power restoration resources, and a power distribution network constraint condition for constraining said power distribution network; on the basis of the objective function and the constraint condition, determining a scheduling policy for the power restoration resources; and on the basis of the scheduling policy, restoring power to said power distribution network. By using the method, the restoration effect of a power distribution network can be improved.

The invention relates to the technical field of image enhancement, in particular to a damaged archive restoration method and device, and the method comprises the following steps: selecting a group of damaged archive images and undamaged archive images, and collecting a color histogram of the undamaged archive images to obtain color histogram data; and based on the color histogram data, adjusting a color histogram of the damaged archive image through a histogram matching technology, matching histogram distribution of the undamaged archive image, and generating a color correction image. According to the method, through matching correction of the image color histograms of the damaged archives and the undamaged archives, the original color features of the archives are accurately reproduced, the interference of human subjective factors on the restoration result is effectively reduced, and the authenticity and color consistency of the restored image are improved; due to the introduction of the local adaptive enhancement technology, the contrast ratio of each local area of the archive image is more prominent, and the problem of overexposure or detail missing possibly caused by overall unified enhancement is avoided.

The invention relates to a cultural relic virtual restoration method and device based on a digital twinning technology, equipment and a medium. The method comprises the following steps: acquiring original data of overseas lost cultural relics; performing standardized preprocessing on the original data to obtain a cultural relic data packet; performing three-dimensional geometric reconstruction based on the cultural relic data packet to obtain a cultural relic three-dimensional digital model; identifying diseases appearing in the cultural relic three-dimensional digital model through an image segmentation model to obtain a cultural relic disease list; based on the cultural relic disease list, predicting the risk of the structural stability of the cultural relic three-dimensional digital model through mechanical simulation to obtain a cultural relic risk prediction map; and based on the cultural relic disease list and the cultural relic risk prediction map, parallel generation of restoration strategies with different emphasis points is carried out, and a virtual restoration scheme is obtained. By adopting the method, the initial state of the cultural relic can be reproduced in the virtual environment, the restoration effect can be reviewed, and the potential risk can be accurately predicted.

The invention provides a mining area geology restoration method. The mining area geology restoration method comprises the steps that environmental parameters and reaction state data of a to-be-restored mining area are acquired through a sensing network; based on the environmental parameters and the reaction state data, digital twinborn bodies are constructed and run, and the digital twinborn bodies are used for simulating the migration of carbon dioxide in the to-be-restored mining area and the reaction process of the carbon dioxide and minerals; generating an injection strategy for controlling a carbon sequestration reaction process based on a simulation result of the digital twin; according to the injection strategy, injection equipment is controlled to inject a reaction medium into the to-be-repaired mining area; and performing monitoring and fault prediction on the operation state of the injection equipment, and generating a maintenance instruction. According to the invention, intelligence, precision, high efficiency and multi-target collaboration of the geological restoration process of the mining area are realized.