228 results about "Dictionary learning" patented technology

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses a cancer image denoising method and system based on a multi-scale neural network, and the method comprises the steps: carrying out the adaptive segmentation of a cancer image, carrying out the noise estimation and statistical analysis of each segmented region, and automatically adjusting the denoising parameters; performing multi-scale feature extraction by adopting wavelet transform and pyramid decomposition, and analyzing and processing the image after parameter adjustment; performing quality evaluation and distortion detection on the image after multi-scale processing, automatically identifying and positioning artifacts and distortion, and correcting and compensating interference by adopting a point spread function inversion and Wiener filtering method to obtain a corrected image; and after histogram equalization and contrast-limited adaptive histogram equalization are applied to the corrected image, denoising processing is carried out by using a convolutional neural network in combination with sparse representation and a dictionary learning algorithm, and a denoising result is obtained. According to the method, noise in different types of cancer images can be effectively removed, meanwhile, key diagnosis information is reserved, and the image quality and definition are improved.

The invention discloses a data processing method for industrial equipment for industrial control Internet of Things, which comprises the following steps: acquiring industrial equipment sensor data, and carrying out standardization processing to obtain a standardized matrix; initializing a sparse dictionary learning model, setting an initial dictionary structure and a sparse coefficient, and executing preliminary sparse decomposition; constructing a flower pollination algorithm search space, optimizing a layering number, a regular weight and a switching threshold, and updating dictionary parameters; calculating an optimal sparse coefficient matrix, and performing sparse reconstruction processing; and carrying out data denoising, compression and feature extraction, and outputting a final processing result. According to the method, the flower pollination optimization algorithm and the sparse representation dictionary learning model are fused, so that efficient denoising, compression and abnormal feature extraction processing of the industrial equipment data are realized.

The invention provides a power big data-based sparsity feature adaptive compression method, which comprises the steps of adaptively optimizing a sensing matrix through a dictionary learning method aiming at a screened multi-granularity feature subset, introducing a sparse regularization item to enhance the sparsity of data reconstruction, and according to a missing mode of a missing value and context information, carrying out adaptive compression on the sparse feature of the power big data. Establishing a mapping relationship among the compression ratio, the reconstruction error and the feature subset, and determining a self-adaptive compression strategy; based on a self-adaptive compression strategy, a compressed sensing method is adopted to carry out self-adaptive compression on the multi-time-granularity power data, the compression ratio is dynamically adjusted according to reconstruction error feedback, the data compression and reconstruction quality is balanced, and the reconstructed multi-granularity power data is synchronized according to timestamp precision.

The invention belongs to the field of magnetic resonance measurement signal noise filtering, and particularly relates to a ground magnetic resonance signal sparse denoising method of a combined dictionary, which comprises the following steps: reconstructing a power frequency harmonic component of a magnetic resonance signal according to frequency bands, and eliminating power frequency harmonics from the magnetic resonance signal to obtain a power frequency-removed signal; constructing a track matrix Z by adopting a Gaussian random matrix, constructing a track matrix Y by adopting a power frequency-removed signal, taking first K columns of the track matrix Z and normalizing to obtain an initial dictionary D, and using the track matrix Y as an original sample for constructing a sparse coefficient matrix X; updating the initial dictionary D and the sparse coefficient matrix X through K-SVD dictionary learning, and reconstructing a track matrix by adopting the updated dictionary # imgabs0 # and the sparse coefficient matrix # imgabs1 # to obtain a track matrix W; and obtaining a pure magnetic resonance signal by taking the first row of the track matrix W, and realizing effective removal of MRS signal noise in a complex electromagnetic interference scene under the condition of single signal acquisition.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention relates to the technical field of condition prediction, and particularly discloses a condition score prediction method for Alzheimer's disease, which comprises the following steps: S1, acquiring longitudinal multi-time-point MRI image data and corresponding clinical score data of a target subject; s2, processing to obtain a feature matrix of the brain region and brain region data and clinical scores at different time points; s3, performing sparse representation on the feature matrix by using a dictionary learning algorithm to obtain a sparse representation matrix; s4, constructing an SLSTM, taking the sparse representation matrixes of the baseline moment and the M06 and M12 time points as input, and training a model to predict clinical scores of subsequent time points M18, M24 and M36; according to the method, sparse representation is performed on MRI image data through dictionary learning, redundant features are removed, features highly related to disease change are extracted, and a long-term dependency relationship in a longitudinal time sequence is captured in combination with a double-layer stacked LSTM structure, so that the model can more accurately predict clinical scores of future time points.

The invention discloses an image super-resolution reconstruction method based on compressed sensing, and belongs to the field of image reconstruction. The problems that the updating speed of existing hardware cannot meet the requirement for signal precision, and on the premise that the hardware cost is not increased, an image with higher definition cannot be reconstructed are solved. The invention discloses an image super-resolution reconstruction method based on compressed sensing, and the method comprises the steps: designing a dictionary learning process, and obtaining high-frequency component parameters needed in image restoration; comprising the steps of constructing a training data set and performing dictionary training; wherein a corresponding low-resolution image is obtained by carrying out quality reduction and scale reduction calculation on an image so as to carry out a process of training a data set; constructing an over-complete dictionary pair by using a K-SVD algorithm, and training the dictionary; firstly, a reduced low-frequency component is obtained, and then the resolution is reconstructed. According to the method, the GPSR algorithm is utilized, the limitation of fixed sparseness of a traditional OMP algorithm is overcome, a more accurate sparse coefficient is obtained, and the precision of the reconstructed image is further improved by adopting a bilateral filtering mode.

Methods and apparatus are provided for encoding and subsequent decoding of video data by using a learnt video specific dictionary for implicit neural compression. An implicit neural representation comprising a head layer and a tail layer is used with approximations to the head layer parameters. The approximations are determined with combinations of atoms of a learnt video specific dictionary. In one embodiment, the head layer approximations and tail layer parameters are encoded in a bitstream. The dictionary is learnt at the decoder. In another embodiment, a dictionary is sent in the bitstream. At decoding, a reconstructed image is computed using transmitted INR parameters.

The invention provides a sparse representation-based low-bit-rate video coding method, which comprises the following steps of: after a coding end acquires an input video, carrying out frame segmentation sampling on the input video to obtain a video frame sequence, and carrying out block segmentation on elements in the video frame sequence to obtain an image block sequence; processing elements in the image block sequence based on a dictionary learning algorithm and a sparse representation algorithm of gradient sparsity to obtain a sparse representation coefficient; sequentially performing quantization processing and entropy coding processing on the sparse representation system to obtain a compressed bit stream, and storing and transmitting the compressed bit stream; obtaining a compressed bit stream, and sequentially performing entropy decoding processing and inverse quantization processing on the bit stream to obtain an original sparse representation coefficient; processing the original sparse representation coefficient based on a dictionary learning algorithm and a sparse decoding algorithm of gradient sparsity to obtain a reconstructed image block; merging the plurality of reconstructed image blocks to obtain a reconstructed video frame; and carrying out frame combination on the plurality of reconstructed video frames to obtain a reconstructed input video.

The invention discloses a face recognition method based on sparse coding and dictionary learning. The method comprises the following steps: S1, collecting and preprocessing a standardized training image and a to-be-recognized face image; s2, training a dictionary set, labeling categories, and initializing and optimizing the dictionary; s3, performing sparse coding, and solving a sparse coefficient by using the optimization dictionary; s4, carrying out regional feature division and fusion of multi-region sparse coding, weighted compression and interactive modeling; s5, performing fusion feature normalization and classification, and performing joint discrimination by a reconstruction error and a discrimination classifier; s6, performing confidence evaluation and dictionary updating, and triggering an incremental learning updating mechanism with low confidence; and S7, performing model deployment and real-time identification, and deploying an optimization dictionary and a classification module to perform identification output. The invention provides a face recognition method suitable for a complex environment, and the method has the advantages of high precision, strong robustness and good practicability.

Existing Convolutional Dictionary Learning (CDL) based machine fault classification do not utilize label information while learning the dictionary, hence the representation learned are not class-discriminative. Method and system disclosed herein provide a label-consistent convolutional dictionary learning approach for machine fault classification. The approach involves generating a training data for a classifier, wherein coefficients forming a plurality of class-discriminative features form the training data. The training data is then used to train a classifier, which is then used to perform machine fault classification for a given test data.

The invention discloses a single-source-domain generalization medical image segmentation method and device based on a shape dictionary, and relates to the technical field of computers, and the method comprises a shape dictionary learning step, an overall segmentation network construction step, an overall segmentation network training step and a medical image segmentation step. According to the invention, by combining a domain invariant shape reference segmentation model and random feature enhanced domain specific generalization information learning, on the premise of single source domain data training, the learning ability of a network model to domain invariant features and simulated unknown domain specific features is improved; and the generalization ability and segmentation accuracy of the model in various unknown target domains are improved to the greatest extent. According to the method provided by the invention, only single-source-domain data is needed, and the problem of single-source-domain generalization SDG of a medical image segmentation task can be well relieved.

The invention discloses an industrial fault detection method based on structured augmented dictionary learning, and the method comprises the steps: firstly processing a fault-free component through a basic dictionary, and introducing an additional low-dimensional sparse dictionary for precisely reconstructing a fault related part, thereby avoiding the decentralization of fault information. Secondly, in order to ensure that a fault-free part is in a statistical control state, a steady-state statistical characteristic and a manifold structure constraint are embedded into a basic dictionary coefficient, and meanwhile, a fault-related characteristic variable is accurately selected through a hard sparse constraint, so that a fault-free signal in an industrial process is effectively prevented from being interfered; therefore, accurate separation of the fault and the normal component of the signal is realized in sparse decoding, so that the fault mode is accurately revealed; in addition, an aggregation method based on a moving window is adopted, the sensitivity and detection efficiency of initial faults in the industrial process are remarkably improved, and an efficient and reliable fault diagnosis solution is provided for industrial application.

The invention discloses a multi-frequency near-field millimeter wave super-resolution imaging method based on a compressed sensing model, and relates to the field of super-resolution imaging. Aiming at the problems in the prior art, the invention provides a multi-frequency near-field millimeter wave super-resolution imaging method, the method is based on a compressed sensing model, the selection of multi-frequency near-field millimeter waves is used for improving the stability of imaging quality, and the compressed sensing model is used for constructing a mixed sparse function. And a wavelet transform domain norm and a TV (Total variation) operator are taken as optimization targets, so that the reconstruction of the high-resolution image is realized. Aiming at a subsequent image processing process of a two-dimensional near-field millimeter wave imaging system, under the condition of shortening data sampling time, a high-resolution image is reconstructed from low-resolution sampling data, and redundant hardware, a large number of image data sets and a dictionary learning process are not needed.

The invention discloses a foundation pit support deformation real-time monitoring method based on edge calculation, and aims to solve the problems of multi-node time synchronization and clock drift compensation caused by unavailability or poor quality of satellite navigation and precise time protocols in an underground or shielded environment. According to the method, through event dictionary learning, partition time slot scheduling and safety coding micro excitation time synchronization, matched filtering decoding, multi-scale arrival time estimation, paired time difference robust fusion, frequency priority and phase speed limit correction and network disconnection time holding, the time synchronization efficiency is improved; the technical effects that multi-source data alignment, deformation field reconstruction and graded early warning are completed on the unified virtual reference time scale, and high-precision synchronization and online drift compensation are obtained without depending on the external time service condition are achieved.

The invention relates to a fault arc-caused fire early warning method based on multi-sensor fusion, and belongs to the field of fault arc fire. The method comprises the following steps: establishing an arc electrical fire cause fault simulation test platform, selecting a typical single load and a combined load to perform a test before normal operation of a line and the occurrence of an arc cause fire, and establishing a fault arc waveform database and an arc fire database; the power-on detection method for the fault arc detection device is based on dictionary learning, current characteristics are represented through a sparse matrix, and fault classification is carried out in combination with an SVM. The fluctuation range of a plurality of characteristic quantities of the line in a normal state and when a fault arc occurs is analyzed, a threshold value is determined according to the periodic increase ratio of the characteristic quantities in different states, a final combined load detection threshold value is formed by complementation of a plurality of threshold values, and fault arc detection is realized; and based on the arc fire database, training a double-layer LSTM model under Bayesian optimization, and identifying multi-sensor signals in a time window to realize fire early warning.

The invention provides a time-varying frequency spectrum map compression surveying and mapping method based on dictionary learning and smooth regularization, and the method comprises the steps: building a sparse base psi through the path loss between two points in a spatial frequency domain, carrying out the sparse representation of a signal in the spatial frequency domain, and building a compression sensing model of a three-dimensional space through combining with a measurement matrix; a measurement matrix in the compressed sensing model is optimized, singular value decomposition (SVD) is used for decomposing psi, sparse bases psi m corresponding to the first m maximum eigenvalues are selected, and an optimized measurement matrix is obtained after m times of iteration; the optimized measurement matrix is substituted into a compressed sensing model, an O-KSVD dictionary is adopted to learn and update a sparse base psi, and spectrum data are recovered; finally, sparse regularization and weighted smoothing regularization are combined to achieve the minimum Euclidean distance between adjacent points in the reconstructed spectrum map, smoothing regularization is used for driving the adjacent points in the reconstructed spectrum map to be similar, sparse representation in the spectrum map is updated through iteration till convergence, and actual spectrum mapping is obtained.

The invention discloses a multi-working-condition process monitoring method and system based on zero-forgetting continuous dictionary learning, and the method comprises the steps: carrying out the offline modeling: firstly, carrying out the dictionary learning through employing an initial working condition data set, obtaining an initial dictionary, carrying out the decomposition, obtaining a low-rank matrix of an initial working condition, and calculating the control limit of the initial working condition through calculating a sample reconstruction error; performing incremental updating on the low-rank matrix obtained by learning the old working condition by using the new working condition data set to obtain a low-rank matrix of a new working condition, and calculating a control limit of the new working condition; on-line monitoring comprises the following steps: firstly, a weight selector is used for distributing weights for a low-rank matrix of a learned working condition according to monitoring data, and a monitoring dictionary is constructed in a self-adaptive manner; reconstructing the monitoring data by using the monitoring dictionary, solving a reconstruction error, and further judging the working condition of the monitoring data; and finally, calculating a fault detection statistic according to the reconstruction error and the control limit of the corresponding working condition, and judging whether the monitoring data is abnormal or not according to the statistic. According to the invention, accurate monitoring of a multi-working-condition process is realized.

The invention relates to the technical field of data processing, in particular to a covariance inverse matrix recursive updating method for on-satellite hyperspectral anomaly detection, which comprises the following steps: acquiring a hyperspectral data stream; carrying out adaptive dimension reduction processing on the data stream and loading an initial background model; extracting local background statistics by adopting a sliding window mechanism, generating a background spectrum dictionary by utilizing online dictionary learning, calculating a reconstruction error between a current pixel and the dictionary and a Mahalanobis distance between the current pixel and a background model, and fusing to generate an abnormal score; performing abnormal confidence coefficient evaluation by combining the spatial context information and the spectral angle matching degree, and updating a spectral mean vector by using an exponential weighted moving average algorithm based on non-abnormal pixel data; and the updated model is injected into the next round of processing to form a recursive chain. According to the method, the high calculation complexity of direct inversion of a covariance matrix is avoided, real-time anomaly detection on a satellite is realized, the downloading amount of original data is remarkably reduced, and the congestion of a satellite-ground communication link is relieved.

The invention discloses an abnormal waveform matching method for electric energy meter voltage abnormity identification, and relates to the technical field of waveform matching, and the method comprises the steps: collecting a historical abnormal waveform, extracting a feature vector, and constructing an abnormal waveform dictionary through a dynamic dictionary learning algorithm; performing coverage analysis on historical abnormal waveforms, and screening core features to establish a simplified feature set; collecting a to-be-recognized voltage waveform in real time, extracting a feature vector to be matched with the simplified feature set, and primarily judging whether dominant anomaly exists or not; if yes, candidate template types are screened through the characteristic deviation ratio, and abnormal types are judged; if the dominant anomaly does not exist, a normal waveform feature baseline model is constructed, deviation analysis is carried out to judge whether the implicit anomaly exists or not, and if the implicit anomaly exists, the abnormal waveform dictionary is iteratively optimized in combination with the to-be-recognized waveform features; the method can effectively identify the voltage abnormity of the electric energy meter, balance the identification precision and the calculation efficiency, and adapt to a novel abnormal mode and equipment gradual change fault characteristics.

The invention relates to a deep energy level transient spectrum data processing method based on dictionary learning. The method comprises the following steps: acquiring transient capacitance signals of a sample at different temperatures by using deep energy level transient spectrum equipment; establishing an Arrhenius equation that the defect emissivity changes along with the temperature; the defect emissivity under each group of values is calculated according to all possible values of the coefficient of the exhaustion Arrhenius equation; constructing a dictionary matrix based on the defect emissivity amplitude introducing Arrhenius constraint, and reconstructing a transient capacitance signal by using the dictionary matrix; and constructing a convex optimization problem, solving a dictionary index variable which enables a reconstruction signal of the transient capacitor to be closest to an acquisition signal, and extracting a non-zero solution in the dictionary index variable to obtain a corresponding defect emissivity amplitude spectrum. According to the method, the detection precision of the deep energy level defect can be improved by introducing the Arrhenius constraint relation.

The invention belongs to the communication technology, and particularly relates to a GNSS receiver array anti-interference method combining multivariate variational mode decomposition and dictionary learning, which comprises the following steps: obtaining each intrinsic mode component after an array receiving signal is subjected to multivariate variational mode decomposition, and reconstructing a noise-free signal after effective mode selection; and carrying out dictionary learning by using a noise-free signal, reconstructing an interference signal according to a trained dictionary, and subtracting the reconstructed interference signal from an array receiving signal to realize suppression of the interference signal. The method can effectively relieve the influence of noise components on signals, and guarantees the interference recognition precision of a subsequent algorithm. And the adopted dictionary learning algorithm can effectively identify interference signals of known types.

The invention discloses an X-ray diffraction spectrum sparse reconstruction method and system based on a dynamic dictionary, and the method comprises the steps: generating an initial dictionary, and carrying out the dynamic optimization and updating of the dictionary, and obtaining a sparse dictionary; selecting an X-ray diffraction scanning angle subset based on the sparse dictionary, constructing an observation matrix, encoding an observation process, and obtaining compressed and sampled X-ray diffraction projection data; and sparse vectors are obtained from the obtained X-ray diffraction projection data after compressed sampling, and a reconstruction spectrum is obtained. And the accuracy of sparse representation is improved. The invention provides a high-throughput XRD (X-Ray Diffraction) material spectrogram analysis method combining dynamic dictionary learning, a compressed sensing theory and deep learning, and aims to improve the analysis efficiency and precision of crystal structure recognition and phase composition in new material research and development.

The present invention discloses a sparse aperture ISAR maneuvering target imaging and calibration method, comprising the following steps: establishing a maneuvering target ISAR sparse aperture signal model, characterizing the maneuverability of the target by constructing a variable scale non-uniform Fourier two-dimensional dictionary containing MTRC; constructing a parameterized sparse imaging model by maximum a posteriori estimation, expressed as a cost function of joint target parameter estimation and sparse imaging; finding the target rotation parameter by the minimum entropy method, and implementing a rough estimation of the maneuvering target rotation parameter by performing parameter iteration by the coordinate descent method; and implementing high-quality imaging with MTRC correction and accurate parameter estimation by combining sparse ISAR imaging and target rotation parameter estimation, decomposing into sparse imaging and dictionary learning, and iterating and solving in sequence. The present invention realizes the use of sparse aperture echo data to solve the problem of high-resolution imaging of ISAR maneuvering targets, greatly increasing the implementation scope and application scenarios of radar imaging.

The invention discloses a power distribution network fault signal reconstruction method, system and device based on K-SVD and orthogonal matching pursuit, and a medium, and belongs to the technical field of power systems, and the method comprises the steps: obtaining a fault signal from a monitoring device of a power distribution network, and carrying out the preprocessing of the signal; a K-SVD algorithm is used for dictionary learning, a sparse dictionary suitable for power distribution network fault signals is generated, and the dictionary is optimized through training of a signal data set; performing sparse representation on the signal by adopting an orthogonal matching pursuit algorithm, and realizing sparse decomposition of the signal by searching an optimal sparse representation coefficient; based on the sparse representation coefficient, reconstructing the signal from the sparse dictionary, calculating a reconstruction error, comparing the reconstruction error with an actual fault signal, evaluating a reconstruction effect and an error, and performing parameter optimization adjustment; and accurately judging the type of the fault according to the reconstructed signal. According to the method, the key information of the fault signal is effectively extracted in a complex noise environment, and the signal reconstruction precision is remarkably improved.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

The invention discloses a noise reduction method based on double sparse dictionary self-adaption and a bearing fault diagnosis method and system. The method comprises the following steps: performing wavelet decomposition on a signal to be subjected to noise reduction to obtain a high-frequency signal and a low-frequency signal, and constructing a high-frequency matrix and a low-frequency matrix; dDTF dictionary learning based on threshold self-adaption is carried out on the high-frequency matrix and the low-frequency matrix, namely, an initial dictionary is set, learning is carried out to obtain an initial sparse coefficient matrix, and self-adaption threshold updating is carried out on the initial sparse coefficient matrix, namely, the initial sparse coefficient matrix is updated according to the sequence from large to small; selecting a sparse coefficient as a dynamic threshold value in an iteration process to update an initial sparse coefficient; determining a final threshold value to obtain a final sparse coefficient matrix, updating the dictionary again to obtain sub-signals, and finally performing inverse transformation and matrix rearrangement inverse operation on the obtained sub-signals to obtain noise reduction signals. According to the method, wavelet decomposition and DDTF are combined to construct a double sparse mode, and the sparse representation capability of a fixed base is effectively improved.

This disclosure relates generally to the field of image processing, and, more particularly, to a method and system for Multimodal Image Super-Resolution (MISR) using convolutional dictionary learning. Existing sparse representation learning based techniques for MISR have certain limitations which impact quality of the reconstructed image. The present disclosure performs MISR using convolutional dictionaries which are translation invariant. Low-resolution image of the target modality and high-resolution image of the guidance modality are modelled using their respective convolutional dictionaries and associated coefficients. Additionally, two coupling convolutional dictionaries are learned to model the relationship between them and synthesize the high-resolution image of the target modality more efficiently.

The invention belongs to the technical field of image analysis and processing, and discloses a woven fabric texture reconstruction method based on deep shared convolutional dictionary learning, and the method specifically comprises the steps: (1) constructing a deep shared convolutional dictionary learning model; (2) decomposing an overall optimization problem of the constructed deep shared convolutional dictionary learning model into N independent optimization problems; (3) rewriting the N independent optimization problems into three sub-optimization problems with constraints, and converting the three sub-optimization problems into optimization problems without constraints according to an alternating direction multiplier method; (4) inputting woven fabric texture sample images in the training set, and iteratively solving an optimization problem without constraints by adopting a multi-stage training strategy; and (5) inputting the test set image into the deep shared convolutional dictionary learning model, outputting the corresponding adaptive convolutional dictionary and convolutional coding coefficient, and finally calculating to obtain a reconstructed image. According to the method, high-quality reconstruction of a complex woven fabric texture image and general representation of an untrained woven fabric texture image are realized.