Journal articles: 'Convolutional dictionary'

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Song, Andrew, Bahareh Tolooshams, and Demba Ba. "Gaussian Process Convolutional Dictionary Learning." IEEE Signal Processing Letters 29 (2022): 95–99. http://dx.doi.org/10.1109/lsp.2021.3127471.

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Gao, Fangyuan, Xin Deng, Mai Xu, Jingyi Xu, and Pier Luigi Dragotti. "Multi-Modal Convolutional Dictionary Learning." IEEE Transactions on Image Processing 31 (2022): 1325–39. http://dx.doi.org/10.1109/tip.2022.3141251.

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Yoon, Jongsu, and Yoonsik Choe. "Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding." Applied Sciences 10, no. 12 (June 26, 2020): 4395. http://dx.doi.org/10.3390/app10124395.

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Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement.

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Chun, Il Yong, and Jeffrey A. Fessler. "Convolutional Dictionary Learning: Acceleration and Convergence." IEEE Transactions on Image Processing 27, no. 4 (April 2018): 1697–712. http://dx.doi.org/10.1109/tip.2017.2761545.

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Song, Andrew H., Francisco J. Flores, and Demba Ba. "Convolutional Dictionary Learning With Grid Refinement." IEEE Transactions on Signal Processing 68 (2020): 2558–73. http://dx.doi.org/10.1109/tsp.2020.2986897.

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Li, Jing, Xiao Wei, Fengpin Wang, and Jinjia Wang. "IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning." Electronics 10, no. 23 (December 3, 2021): 3021. http://dx.doi.org/10.3390/electronics10233021.

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Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM).

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Molla, Md Rony, and Ma Jian Fen. "Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification." International Journal of Advanced Engineering Research and Science 10, no. 12 (2023): 096–104. http://dx.doi.org/10.22161/ijaers.1012.10.

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Multiple Instance Learning (MIL) is commonly utilized in weakly supervised whole slide image (WSI) classification. MIL techniques typically involve a feature embedding step using a pretrained feature extractor, then an aggregator that aggregates the embedded instances into predictions. Current efforts aim to enhance these sections by refining feature embeddings through self-supervised pretraining and modeling correlations between instances. In this paper, we propose a convolutional sparsely coded MIL (CSCMIL) that utilizes convolutional sparse dictionary learning to simultaneously address these two aspects. Sparse dictionary learning consists of filters or kernels that are applied with convolutional operations and utilizes an overly comprehensive dictionary to represent instances as sparse linear combinations of atoms, thereby capturing their similarities. Straightforwardly built into existing MIL frameworks, the suggested CSC module has an affordable computation cost. Experiments on various datasets showed that the suggested CSC module improved performance by 3.85% in AUC and 4.50% in accuracy, equivalent to the SimCLR pretraining (4.21% and 4.98%) significantly of current MIL approaches.

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Martin-del-Campo, Sergio, Fredrik Sandin, and Stephan Schnabel. "Algorithmic performance constraints for wind turbine condition monitoring via convolutional sparse coding with dictionary learning." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 4 (January 5, 2021): 660–75. http://dx.doi.org/10.1177/1748006x20984260.

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We analyze vibration signals from wind turbines with dictionary learning and investigate the relation between dictionary distances and faults occurring in a wind turbine output shaft rolling element bearing and gearbox under different data and compute constraints. Dictionary learning is an unsupervised machine learning method for signal processing, which permits learning a set of signal-specific features that have been used to monitor the condition of rotating machines, including wind turbines. Dictionary distance is one such feature, and its effectiveness depends on an adequate selection of the dictionary learning hyperparameters and the data availability, which typically is constrained in condition monitoring systems for remotely located wind farms. Here we evaluate the characteristics of the dictionary distance feature under healthy and faulty conditions of the wind turbines using different options for the selection of the pretrained dictionary, the sparsity of the signal model which determines the compute requirements, and the interval between data samples. Furthermore, we compare the dictionary distance feature to the typical time-domain features used in condition monitoring. We find that the dictionary distance based feature of a faulty wind turbine deviates by a factor of two or more from the population distribution several weeks before the gearbox bearing fault was reported, using a data sampling interval as long as 24 h and a model sparsity as low as 2.5%.

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Cheng, Ruihong, Huajun Wang, and Ping Luo. "Remote sensing image super-resolution using multi-scale convolutional sparse coding network." PLOS ONE 17, no. 10 (October 26, 2022): e0276648. http://dx.doi.org/10.1371/journal.pone.0276648.

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With the development of convolutional neural networks, impressive success has been achieved in remote sensing image super-resolution. However, the performance of super-resolution reconstruction is unsatisfactory due to the lack of details in remote sensing images when compared to natural images. Therefore, this paper presents a novel multiscale convolutional sparse coding network (MCSCN) to carry out the remote sensing images SR reconstruction with rich details. The MCSCN, which consists of a multiscale convolutional sparse coding module (MCSCM) with dictionary convolution units, can improve the extraction of high frequency features. We can obtain more plentiful feature information by combining multiple sizes of sparse features. Finally, a layer based on sub-pixel convolution that combines global and local features takes as the reconstruction block. The experimental results show that the MCSCN gains an advantage over several existing state-of-the-art methods in terms of peak signal-to-noise ratio and structural similarity.

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Xu, Yulong, Yingying Zhao, Hao Rong, Fangfang Liu, Yali Lv, and Honglei Zhu. "Semantic Analysis of Public Health Medical Issues Based on Convolution Neural Networks." Mobile Information Systems 2022 (August 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/2392207.

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Text mining and semantic analysis of medical public health issues are the main points for intelligent medical interaction, but less relevant research has been done on them. This article conceives a convolutional neural network for the semantic classification of public health medical issues. The dual convolution layer is used to further reduce the dimension of the data, extract more in-depth information from the data, and map the features. Each convolution layer includes several convolution nuclei to extract semantic characteristics, and then, the complete connection layer is input to the classifier to obtain the results of the classification. To check the classification effect, the dictionary artificial construction and the double hidden-layers neuronal network are used for semantic classification, and the three methods are compared and tested on the six real datasets. The experimental results show that when the quality of the dataset is high, the convolution neural network method proposed in this paper exceeds the last two methods. The proposed method is higher than the construction of the artificial dictionary and the double hidden-layers neural network in the recall rate: 0.153 and 0.037, and greater than 0.07 and 0.01 for the F1 measure rate, respectively. When the quality of the dataset is general, the models of the three methods do not give good classification results. Finally, it is concluded that the convolutional neural network method conceived has a good semantic recognition performance in public health medical issues.

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Mansha, Sameen, Hoang Thanh Lam, Hongzhi Yin, Faisal Kamiran, and Mohsen Ali. "Layered convolutional dictionary learning for sparse coding itemsets." World Wide Web 22, no. 5 (May 11, 2018): 2225–39. http://dx.doi.org/10.1007/s11280-018-0565-2.

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Zhou, Tong, Changyin Dong, Zhen Wang, Bo Chang, Junshu Song, Bin Shen, Baodi Liu, and Pengfei He. "Convolutional Shared Dictionary Module for Few-shot Learning." Electronic Imaging 37, no. 8 (February 2, 2025): 265–1. https://doi.org/10.2352/ei.2025.37.8.image-265.

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Mushtaq, Zaid Bin, Shoaib Mohd Nasti, Chaman Verma, Maria Simona Raboca, Neerendra Kumar, and Samiah Jan Nasti. "Super Resolution for Noisy Images Using Convolutional Neural Networks." Mathematics 10, no. 5 (February 28, 2022): 777. http://dx.doi.org/10.3390/math10050777.

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The images in high resolution contain more useful information than the images in low resolution. Thus, high-resolution digital images are preferred over low-resolution images. Image super-resolution is one of the principal techniques for generating high-resolution images. The major advantages of super-resolution methods are that they are economical, independent of the image capture devices, and can be statically used. In this paper, a single-image super-resolution network model based on convolutional neural networks is proposed by combining conventional autoencoder and residual neural network approaches. A convolutional neural network-based dictionary method is used to train low-resolution input images for high-resolution images. In addition, a linear refined unit thresholds the convolutional neural network output to provide a better low-resolution image dictionary. Autoencoders aid in the removal of noise from images and the enhancement of their quality. Secondly, the residual neural network model processes it further to create a high-resolution image. The experimental results demonstrate the outstanding performance of our proposed method compared to other traditional methods. The proposed method produces clearer and more detailed high-resolution images, as they are important in real-life applications. Moreover, it has the advantage of combining convolutional neural network-based dictionary learning, autoencoder image enhancement, and noise removal. Furthermore, residual neural network training with improved preprocessing creates an efficient and versatile single-image super-resolution network.

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Yin, Luqiao, Wenqing Gao, and Jingjing Liu. "Deep Convolutional Dictionary Learning Denoising Method Based on Distributed Image Patches." Electronics 13, no. 7 (March 28, 2024): 1266. http://dx.doi.org/10.3390/electronics13071266.

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To address susceptibility to noise interference in Micro-LED displays, a deep convolutional dictionary learning denoising method based on distributed image patches is proposed in this paper. In the preprocessing stage, the entire image is partitioned into locally consistent image patches, and a dictionary is learned based on the non-local self-similar sparse representation of distributed image patches. Subsequently, a convolutional dictionary learning method is employed for global self-similarity matching. Local constraints and global constraints are combined for effective denoising, and the final denoising optimization algorithm is obtained based on the confidence-weighted fusion technique. The experimental results demonstrate that compared with traditional denoising methods, the proposed denoising method effectively restores fine-edge details and contour information in images. Moreover, it exhibits superior performance in terms of PSNR and SSIM. Particularly noteworthy is its performance on the grayscale dataset Set12. When evaluated with Gaussian noise σ=50, it outperforms DCDicL by 3.87 dB in the PSNR and 0.0012 in SSIM.

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Ghansah, Benjamin. "Convolutional Locality-Sensitive Dictionary Learning for Facial Expressions Detection." International Journal of Data Analytics 3, no. 1 (January 2022): 1–28. http://dx.doi.org/10.4018/ijda.297520.

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Facial Expression (FE) detection is a popular research area, particularly in the field of Image Classification, Pattern Recognition and Computer Vision. Sparse Representation (SR) and Dictionary Learning (DL) have significantly enhanced the classification performance of image recognition and also resolved the problem of the nonlinear distribution of face images and its implementation with DL. However, the locality structure of face image data containing more discriminative information, which is very critical for classification has not been fully explored by state-of-the-art existing SR-based approaches. Furthermore, similar coding results between test samples and neighboring training data, contained in the feature space are not being fully realized from the image features with similar image categorizations, to effectively capture the embedded discriminative information. In an attempt to resolve the forgoing issues, we propose a novel DL method, Convolutional locality-sensitive Dictionary Learning (CLSDL) for Facial Expression detection.

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MURAMATSU, Shogo. "Convolutional dictionary learning based on filter bank theory : Convolutional network construction using structural constraints." IEICE ESS Fundamentals Review 17, no. 2 (October 1, 2023): 116–25. http://dx.doi.org/10.1587/essfr.17.2_116.

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Humbert, Pierre, Laurent Oudre, Nicolas Vayatis, and Julien Audiffren. "Tensor Convolutional Dictionary Learning With CP Low-Rank Activations." IEEE Transactions on Signal Processing 70 (2022): 785–96. http://dx.doi.org/10.1109/tsp.2021.3135695.

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Peng, Guan-Ju. "Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation." IEEE Transactions on Image Processing 28, no. 7 (July 2019): 3408–22. http://dx.doi.org/10.1109/tip.2019.2896541.

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Garcia-Cardona, Cristina, and Brendt Wohlberg. "Convolutional Dictionary Learning: A Comparative Review and New Algorithms." IEEE Transactions on Computational Imaging 4, no. 3 (September 2018): 366–81. http://dx.doi.org/10.1109/tci.2018.2840334.

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Manimala, M. V. R., C. Dhanunjaya Naidu, and M. N. Giri Prasad. "Convolutional Neural Network for Sparse Reconstruction of MR Images Interposed with Gaussian Noise." Journal of Circuits, Systems and Computers 29, no. 07 (September 26, 2019): 2050116. http://dx.doi.org/10.1142/s0218126620501169.

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Compressive Sensing (CS) reconstructs high-quality images from very few measurements, which are far below Nyquist rate. CS proves to be very useful for acquiring high dimensional image sets like Magnetic Resonance Imaging (MRI). However, the efficiency of MR image reconstruction is affected due to slow acquisition of voluminous k-space data. To improve the quality of reconstructed image and increase the speed of the reconstruction, a novel algorithm namely Adaptive Sparse Reconstruction using Convolution Neural Network AsrCNN has been, proposed for MR Images. AsrCNN employs a CNN, which consists of four convolutional layers and one fully connected layer. The proposed algorithm reconstructs MR images with immense quality, as it is trained over a large dataset with adaptive gradient optimization. The training set consists of [Formula: see text] image patches, which is used to create the dictionary by adaptively updating the weights. Subsequently, the dictionary is employed for recovery of sparse MR images corrupted with Gaussian noise. Patch-based approach in AsrCNN enables MR images of varied sizes to be processed without resizing. Experimental results for AsrCNN show an improvement of 1–5 dB in PSNR over previous state-of-art algorithms. Training has been done on GPU using Convolutional Architecture for Fast Feature Embedding (CAFFE) framework as it reduces significant amount of time in reconstructing images.

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Jia, Bairu, Jindong Xu, Haihua Xing, and Peng Wu. "Remote Sensing Image Fusion Based on Morphological Convolutional Neural Networks with Information Entropy for Optimal Scale." Sensors 22, no. 19 (September 27, 2022): 7339. http://dx.doi.org/10.3390/s22197339.

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Remote sensing image fusion is a fundamental issue in the field of remote sensing. In this paper, we propose a remote sensing image fusion method based on optimal scale morphological convolutional neural networks (CNN) using the principle of entropy from information theory. We use an attentional CNN to fuse the optimal cartoon and texture components of the original images to obtain a high-resolution multispectral image. We obtain the cartoon and texture components using sparse decomposition-morphological component analysis (MCA) with an optimal threshold value determined by calculating the information entropy of the fused image. In the sparse decomposition process, the local discrete cosine transform dictionary and the curvelet transform dictionary compose the MCA dictionary. We sparsely decompose the original remote sensing images into a texture component and a cartoon component at an optimal scale using the information entropy to control the dictionary parameter. Experimental results show that the remote sensing image fusion method proposed in this paper can effectively retain the information of the original image, improve the spatial resolution and spectral fidelity, and provide a new idea for image fusion from the perspective of multi-morphological deep learning.

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Wang, Lifang, Chaoyu Shi, Suzhen Lin, Pinle Qin, and Yanli Wang. "Convolutional Sparse Representation and Local Density Peak Clustering for Medical Image Fusion." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 07 (October 22, 2019): 2057003. http://dx.doi.org/10.1142/s0218001420570037.

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Aiming at the problem of insufficient detail retention in multimodal medical image fusion (MMIF) based on sparse representation (SR), an MMIF method based on density peak clustering and convolution sparse representation (CSR-DPC) is proposed. First, the base layer is obtained based on the registered input image by the averaging filter, and the original image minus the base layer to obtain the detail layer. Second, for retaining the details of the fused image, the detail layer image is fused by CSR to obtain the fused detail layer image, then the base layer image is segmented into several image blocks, and the blocks are clustered by using DPC to obtain some clusters, and each class cluster is trained to obtain a sub-dictionary, and all the sub-dictionaries are fused to obtain an adaptive dictionary. The sparse coefficient is fused through the learned adaptive dictionary, and the fused base layer image is obtained through reconstruction. Finally, fusing the detail layer and the base layer and reconstructing them forms the ultimate fused image. Experiments show that compared to the state-of-the-art two multi-scale transformation methods and five SR methods, the proposed method(CSR-DPC) outperforms the other methods in terms of the image details, the visual quality and the objective evaluation index, which can be helpful for clinical diagnosis and adjuvant treatment.

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Donati, Giovanni, Michele Basso, Graziano A. Manduzio, Marco Mugnaini, Tommaso Pecorella, and Chiara Camerota. "A Convolutional Neural Network for Electrical Fault Recognition in Active Magnetic Bearing Systems." Sensors 23, no. 16 (August 8, 2023): 7023. http://dx.doi.org/10.3390/s23167023.

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Active magnetic bearings are complex mechatronic systems that consist of mechanical, electrical, and software parts, unlike classical rolling bearings. Given the complexity of this type of system, fault detection is a critical process. This paper presents a new and easy way to detect faults based on the use of a fault dictionary and machine learning. The dictionary was built starting from fault signatures consisting of images obtained from the signals available in the system. Subsequently, a convolutional neural network was trained to recognize such fault signature images. The objective of this study was to develop a fault dictionary and a classifier to recognize the most frequent soft electrical faults that affect position sensors and actuators. The proposed method permits, in a computationally convenient way that can be implemented in real time, the determination of which component has failed and what kind of failure has occurred. Therefore, this fault identification system allows determining which countermeasure to adopt in order to enhance the reliability of the system. The performance of this method was assessed by means of a case study concerning a real turbomachine supported by two active magnetic bearings for the oil and gas field. Seventeen fault classes were considered, and the neural network fault classifier reached an accuracy of 93% on the test dataset.

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Jing, Junfeng, Xiaoting Fan, and Pengfei Li. "Patterned fabric defect detection via convolutional matching pursuit dual-dictionary." Optical Engineering 55, no. 5 (May 26, 2016): 053109. http://dx.doi.org/10.1117/1.oe.55.5.053109.

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Liu, Jialin, Cristina Garcia-Cardona, Brendt Wohlberg, and Wotao Yin. "First- and Second-Order Methods for Online Convolutional Dictionary Learning." SIAM Journal on Imaging Sciences 11, no. 2 (January 2018): 1589–628. http://dx.doi.org/10.1137/17m1145689.

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Li, Yuntong, and Lina Liu. "Three-dimensional seismic denoising based on deep convolutional dictionary learning." Results in Applied Mathematics 24 (November 2024): 100516. http://dx.doi.org/10.1016/j.rinam.2024.100516.

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Zhen, Wang, Huo Guanglei, Lan Hai, Hu Jianmin, and Wei Xian. "Fundus image enhancement algorithm based on convolutional dictionary diffusion model." Journal of Image and Graphics 29, no. 8 (2024): 2426–38. http://dx.doi.org/10.11834/jig.230595.

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Zhang, Bing, and Jizhong Liu. "Discriminative Convolutional Sparse Coding of ECG Signals for Automated Recognition of Cardiac Arrhythmias." Mathematics 10, no. 16 (August 11, 2022): 2874. http://dx.doi.org/10.3390/math10162874.

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Electrocardiogram (ECG) is a common and powerful tool for studying heart function and diagnosing several abnormal arrhythmias. In this paper, we present a novel classification model that combines the discriminative convolutional sparse coding (DCSC) framework with the linear support vector machine (LSVM) classification strategy. In the training phase, most existing convolutional sparse coding frameworks are unsupervised in the sense that label information is ignored in the convolutional filter training stage. In this work, we explicitly incorporate a label consistency constraint called “discriminative sparse-code error” into the objective function to learn discriminative dictionary filters for sparse coding. The learned dictionary filters encourage signals from the same class to have similar sparse codes, and signals from different classes to have dissimilar sparse codes. To reduce the computational complexity, we propose to perform a max-pooling operation on the sparse coefficients. Using LSVM as a classifier, we examine the performance of the proposed classification system on the MIT-BIH arrhythmia database in accordance with the AAMI EC57 standard. The experimental results show that the proposed DCSC + LSVM algorithm can obtain 99.32% classification accuracy for cardiac arrhythmia recognition.

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Wang, Xuchu, Fusheng Wang, and Yanmin Niu. "A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection." Sensors 21, no. 11 (May 26, 2021): 3693. http://dx.doi.org/10.3390/s21113693.

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Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

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Zhou, Fengtao, Sheng Huang, and Yun Xing. "Deep Semantic Dictionary Learning for Multi-label Image Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3572–80. http://dx.doi.org/10.1609/aaai.v35i4.16472.

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Compared with single-label image classification, multi-label image classification is more practical and challenging. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. However, these semantic-based methods only take semantic information as type of complements for visual representation without further exploitation. In this paper, we present an innovative path towards the solution of the multi-label image classification which considers it as a dictionary learning task. A novel end-to-end model named Deep Semantic Dictionary Learning (DSDL) is designed. In DSDL, an auto-encoder is applied to generate the semantic dictionary from class-level semantics and then such dictionary is utilized for representing the visual features extracted by Convolutional Neural Network (CNN) with label embeddings. The DSDL provides a simple but elegant way to exploit and reconcile the label, semantic and visual spaces simultaneously via conducting the dictionary learning among them. Moreover, inspired by iterative optimization of traditional dictionary learning, we further devise a novel training strategy named Alternately Parameters Update Strategy (APUS) for optimizing DSDL, which alternately optimizes the representation coefficients and the semantic dictionary in forward and backward propagation. Extensive experimental results on three popular benchmarks demonstrate that our method achieves promising performances in comparison with the state-of-the-arts. Our codes and models have been released.

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Guo, Xiyang. "Research on Mushroom Image Classification Algorithm Based on Deep Sparse Dictionary Learning." Academic Journal of Science and Technology 9, no. 1 (January 20, 2024): 235–40. http://dx.doi.org/10.54097/1f3xnx82.

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The traditional mushroom feature extraction method has low classification efficiency and unsatisfactory effect. Dictionary learning is widely used in image classification. However, the previous work is to learn dictionaries in the original space, which limits the performance of sparse representation classification. In order to solve the problem of spatial redundancy in traditional convolutional neural networks and the weak performance of deep learning in small samples, an improved dictionary learning algorithm, Deep Sparse Dictionary learning (DSDL), is proposed. The input to DSDL is not a matrix gathered from the original grayscale image or a hand-created feature, but rather a relatively deeper feature extraction via a stack autoencoder. Then, a structured dictionary is designed to reconstruct the deep features according to different categories of distinguishing features. In addition, it is necessary to learn the associated structured projection sparse dictionary to ensure that the decoder updates in the direction of the deconvolution operator error is minimal. By utilizing sparse dictionary learning loss functions and autoencoder loss functions, DSDL can simultaneously learn deep latent features and corresponding dictionary pairs. In the testing phase of DSDL, the minimum errors of deep feature and structured projection components for different classes can be directly represented by basic matrix multiplication operations. Experimental results show that the proposed method achieves a good classification effect on mushroom images, which shows the effectiveness of the method.

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Almadani, Murad, Umair bin Waheed, Mudassir Masood, and Yangkang Chen. "Dictionary learning with convolutional structure for seismic data denoising and interpolation." GEOPHYSICS 86, no. 5 (July 27, 2021): V361—V374. http://dx.doi.org/10.1190/geo2019-0689.1.

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Seismic data inevitably suffer from random noise and missing traces in field acquisition. This limits the use of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic data denoising and interpolation. Variants of the patch-based learning technique, such as the K-singular value decomposition (K-SVD) algorithm, have been shown to improve denoising and interpolation performance compared with the analytic transform-based methods. However, patch-based learning algorithms work on overlapping patches of data and do not take the full data into account during reconstruction. In contrast, the data patches (convolutional sparse coding [CSC]) model treats signals globally and, therefore, has shown superior performance over patch-based methods in several image processing applications. As a consequence, we test use of the CSC model for seismic data denoising and interpolation. In particular, we use the local block coordinate descent (LoBCoD) algorithm to reconstruct missing traces and clean seismic data from noisy input. The denoising and interpolation performance of the LoBCoD algorithm has been compared with that of K-SVD and orthogonal matching pursuit (OMP) algorithms using synthetic and field data examples. We have used three quality measures to test the denoising accuracy: the peak signal-to-noise ratio (PS/N), the relative L2-norm of the error (RLNE), and the structural similarity index (SSIM). We find that LoBCoD performs better than K-SVD and OMP for all test cases in improving PS/N and SSIM and in reducing RLNE. These observations suggest a huge potential of the CSC model in seismic data denoising and interpolation applications.

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Wang, Junzhe, Brendt Wohlberg, and R. B. A. Adamson. "Convolutional dictionary learning for blind deconvolution of optical coherence tomography images." Biomedical Optics Express 13, no. 4 (March 3, 2022): 1834. http://dx.doi.org/10.1364/boe.447394.

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Peng, Guan-Ju. "Joint and Direct Optimization for Dictionary Learning in Convolutional Sparse Representation." IEEE Transactions on Neural Networks and Learning Systems 31, no. 2 (February 2020): 559–73. http://dx.doi.org/10.1109/tnnls.2019.2906074.

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Li, Zixu, Genji Yuan, and Jinjiang Li. "DUCD: Deep Unfolding Convolutional-Dictionary network for pansharpening remote sensing image." Expert Systems with Applications 249 (September 2024): 123589. http://dx.doi.org/10.1016/j.eswa.2024.123589.

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He, Liu, Cai Yi, Jianhui Lin, and Andy C. C. Tan. "Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization." Shock and Vibration 2020 (November 18, 2020): 1–27. http://dx.doi.org/10.1155/2020/8879732.

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Wheelset bearing is a critical and easily damaged component of a high-speed train. Wheelset bearing fault diagnosis is of great significance to ensure safe operation of high-speed trains and realize intelligent operation and maintenance. The convolutional sparse coding technique based on the dictionary learning algorithm (CSCT-DLA) provides an effective algorithm framework for extracting the impulses caused by bearing defect. However, dictionary learning is easily affected by foundation vibration and harmonic interference and cannot learn the key structure related to fault impulses. At the same time, the detection performance of fault impulse heavily depends on the selection of parameters in this approach. Union of convolutional dictionary learning algorithm (UC-DLA) is an efficient algorithm in CSCT-DLA. In this paper, UC-DLA is introduced and improved for wheelset bearing fault detection. Finally, a novel bearing fault detection method, adaptive UC-DLA combined with bandwidth optimization (AUC-DLA-BO), is proposed. The mathematical formulation of AUC-DLA-BO is a sort of constrained optimization problem, which can overcome foundation vibration and harmonic interference and adaptively determine parameters related to UC-DLA. The proposed method can detect the fault resonance band adaptively, eliminate the noise with the same frequency band as the fault resonance band, and highlight the bearing fault impulses. Simulated signals and bench tests are used to verify the effectiveness of the proposed method. The results show that AUC-DLA-BO can effectively detect bearing faults and realize the refined analysis of fault behavior.

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Huang, Shuying, Yating Xu, Mingyang Ren, Yong Yang, and Weiguo Wan. "Rain Removal of Single Image Based on Directional Gradient Priors." Applied Sciences 12, no. 22 (November 16, 2022): 11628. http://dx.doi.org/10.3390/app122211628.

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Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to recover clean rain-free images from the rain images. In this paper, we propose a rain removal method based on directional gradient priors, which aims to retain the structural information of the original rain image to the greatest extent possible while removing the rain streaks. First, to solve the problem of residual rain streaks, on the basis of the sparse convolutional coding model, two directional gradient regularization terms are proposed to constrain the direction information of the rain stripe. Then, for the rain layer coding in the directional gradient prior terms, a multi-scale dictionary is designed for convolutional sparse coding to detect rain stripes of different widths. Finally, to obtain a more accurate solution, the alternating direction method of multipliers (ADMM) is used to update the multi-scale dictionary and coding coefficients alternately to obtain a rainless image with rich details. Finally, experiments verify that the proposed algorithm achieves good results both subjectively and objectively.

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Wohlberg, Brendt, and Przemek Wozniak. "PSF Estimation in Crowded Astronomical Imagery as a Convolutional Dictionary Learning Problem." IEEE Signal Processing Letters 28 (2021): 374–78. http://dx.doi.org/10.1109/lsp.2021.3050706.

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Li, Guang, Xianjie Gu, Zhengyong Ren, Qihong Wu, Xiaoqiong Liu, Liang Zhang, Donghan Xiao, and Cong Zhou. "Deep Learning Optimized Dictionary Learning and Its Application in Eliminating Strong Magnetotelluric Noise." Minerals 12, no. 8 (August 12, 2022): 1012. http://dx.doi.org/10.3390/min12081012.

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The noise suppression method based on dictionary learning has shown great potential in magnetotelluric (MT) data processing. However, the constraints used in the existing algorithm’s method need to set manually, which significantly limits its application. To solve this problem, we propose a deep learning optimized dictionary learning denoising method. We use a deep convolutional network to learn the characteristic parameters of high-quality MT data independently and then use them as the constraints for dictionary learning so as to achieve fully adaptive sparse decomposition. The method uses unified parameters for all data and completely eliminates subjective bias, which makes it possible to batch-process MT data using sparse decomposition. The processing results of simulated and field data examples show that the new method has good adaptability and can achieve recognition with high accuracy. After processing with our method, the apparent resistivity and phase curves became smoother and more continuous, and the results were validated by the remote reference method. Our method can be an effective alternative method when no remote reference station is set up or the remote reference processing is not effective.

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Wang, Wenzheng, Yuqi Han, Chenwei Deng, and Zhen Li. "Hyperspectral Image Classification via Deep Structure Dictionary Learning." Remote Sensing 14, no. 9 (May 8, 2022): 2266. http://dx.doi.org/10.3390/rs14092266.

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The construction of diverse dictionaries for sparse representation of hyperspectral image (HSI) classification has been a hot topic over the past few years. However, compared with convolutional neural network (CNN) models, dictionary-based models cannot extract deeper spectral information, which will reduce their performance for HSI classification. Moreover, dictionary-based methods have low discriminative capability, which leads to less accurate classification. To solve the above problems, we propose a deep learning-based structure dictionary for HSI classification in this paper. The core ideas are threefold, as follows: (1) To extract the abundant spectral information, we incorporate deep residual neural networks in dictionary learning and represent input signals in the deep feature domain. (2) To enhance the discriminative ability of the proposed model, we optimize the structure of the dictionary and design sharing constraint in terms of sub-dictionaries. Thus, the general and specific feature of HSI samples can be learned separately. (3) To further enhance classification performance, we design two kinds of loss functions, including coding loss and discriminating loss. The coding loss is used to realize the group sparsity of code coefficients, in which within-class spectral samples can be represented intensively and effectively. The Fisher discriminating loss is used to enforce the sparse representation coefficients with large between-class scatter. Extensive tests performed on hyperspectral dataset with bright prospects prove the developed method to be effective and outperform other existing methods.

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Liu, Fengshuang, Jiachen Ma, and Qiang Wang. "Atom-substituted tensor dictionary learning enhanced convolutional neural network for hyperspectral image classification." Neurocomputing 455 (September 2021): 215–28. http://dx.doi.org/10.1016/j.neucom.2021.05.051.

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Wang, Hong, Yuexiang Li, Nanjun He, Kai Ma, Deyu Meng, and Yefeng Zheng. "DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images." IEEE Transactions on Medical Imaging 41, no. 4 (April 2022): 869–80. http://dx.doi.org/10.1109/tmi.2021.3127074.

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Li, Pengyu, Yali Zhang, Ze Li, and Jinjia Wang. "Iterative shrinkage-thresholding algorithm with inertia and dry friction for convolutional dictionary learning." Digital Signal Processing 140 (August 2023): 104139. http://dx.doi.org/10.1016/j.dsp.2023.104139.

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Wang, Hao, Jingyi Wang, and Zou Fan. "Deep convolutional sparse dictionary learning for bearing fault diagnosis under variable speed condition." Journal of the Franklin Institute 362, no. 1 (January 2025): 107392. http://dx.doi.org/10.1016/j.jfranklin.2024.107392.

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Xu, Mengchao, Qian Liu, Dexuan Sha, Manzhu Yu, Daniel Q. Duffy, William M. Putman, Mark Carroll, Tsengdar Lee, and Chaowei Yang. "PreciPatch: A Dictionary-based Precipitation Downscaling Method." Remote Sensing 12, no. 6 (March 23, 2020): 1030. http://dx.doi.org/10.3390/rs12061030.

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Climate and weather data such as precipitation derived from Global Climate Models (GCMs) and satellite observations are essential for the global and local hydrological assessment. However, most climatic popular precipitation products (with spatial resolutions coarser than 10km) are too coarse for local impact studies and require “downscaling” to obtain higher resolutions. Traditional precipitation downscaling methods such as statistical and dynamic downscaling require an input of additional meteorological variables, and very few are applicable for downscaling hourly precipitation for higher spatial resolution. Based on dynamic dictionary learning, we propose a new downscaling method, PreciPatch, to address this challenge by producing spatially distributed higher resolution precipitation fields with only precipitation input from GCMs at hourly temporal resolution and a large geographical extent. Using aggregated Integrated Multi-satellitE Retrievals for GPM (IMERG) data, an experiment was conducted to evaluate the performance of PreciPatch, in comparison with bicubic interpolation using RainFARM—a stochastic downscaling method, and DeepSD—a Super-Resolution Convolutional Neural Network (SRCNN) based downscaling method. PreciPatch demonstrates better performance than other methods for downscaling short-duration precipitation events (used historical data from 2014 to 2017 as the training set to estimate high-resolution hourly events in 2018).

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Shao, Jie, and Yibo Wang. "Simultaneous inversion of Q and reflectivity using dictionary learning." GEOPHYSICS 86, no. 5 (September 1, 2021): R763—R776. http://dx.doi.org/10.1190/geo2020-0095.1.

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Quality factor ( Q) and reflectivity are two important subsurface properties in seismic data processing and interpretation. They can be calculated simultaneously from a seismic trace corresponding to an anelastic layered model by a simultaneous inversion method based on the nonstationary convolutional model. However, the conventional simultaneous inversion method calculates the optimum Q and reflectivity based on the minimum of the reflectivity sparsity by sweeping each Q value within a predefined range. As a result, the accuracy and computational efficiency of the conventional method depend heavily on the predefined Q value set. To improve the performance of the conventional simultaneous inversion method, we have developed a dictionary learning-based simultaneous inversion of Q and reflectivity. The parametric dictionary learning method is used to update the initial predefined Q value set automatically. The optimum Q and reflectivity are calculated from the updated Q value set based on minimizing not only the sparsity of the reflectivity but also the data residual. Synthetic data and two field data sets are used to test the effectiveness of our method. The results demonstrate that our method can effectively improve the accuracy of these two parameters compared to the conventional simultaneous inversion method. In addition, the dictionary learning method can improve computational efficiency up to approximately seven times when compared to the conventional method with a large predefined dictionary.

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Tanuja, Nukapeyyi. "Medical Image Fusion Using Deep Learning Mechanism." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 128–36. http://dx.doi.org/10.22214/ijraset.2022.39809.

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Abstract: Sparse representation(SR) model named convolutional sparsity based morphological component analysis is introduced for pixel-level medical image fusion. The CS-MCA model can achieve multicomponent and global SRs of source images, by integrating MCA and convolutional sparse representation(CSR) into a unified optimization framework. In the existing method, the CSRs of its gradient and texture components are obtained by the CSMCA model using pre-learned dictionaries. Then for each image component, sparse coefficients of all the source images are merged and then fused component is reconstructed using the corresponding dictionary. In the extension mechanism, we are using deep learning based pyramid decomposition. Now a days deep learning is a very demanding technology. Deep learning is used for image classification, object detection, image segmentation, image restoration. Keywords: CNN, CT, MRI, MCA, CS-MCA.

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Khosravi, Sara, and Abdolah Chalechale. "Recognition of Persian/Arabic Handwritten Words Using a Combination of Convolutional Neural Networks and Autoencoder (AECNN)." Mathematical Problems in Engineering 2022 (July 8, 2022): 1–15. http://dx.doi.org/10.1155/2022/4241016.

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Despite extensive research, recognition of Persian and Arabic manuscripts is still a challenging problem due to the complicated and irregular nature of writing, wide vocabulary, and diversity of handwritings. In Persian and Arabic words, letters are joined together, and signs such as dots are placed above or below letters. In the proposed approach, the words are first decomposed into their constituent subwords to enhance the recognition accuracy. Then the signs of subwords are extracted to develop a dictionary of main subwords and signs. The dictionary is then employed to train a classifier. Since the proposed recognition approach is based on unsigned subwords, the classifier may make a mistake in recognizing some subwords of a word. To overcome this, a new subword fusion algorithm is proposed based on the similarity of the main subwords and signs. Here, convolutional neural networks (CNNs) are utilized to train the classifier. An autoencoder (AE) network is employed to extract appropriate features. Thus, a hybrid network is developed and named AECNN. The known Iranshahr dataset, including nearly 17000 images of handwritten names of 503 cities of Iran, was employed to analyze and test the proposed approach. The resultant recognition accuracy is 91.09%. Therefore, the proposed approach is much more capable than the other methods known in the literature.

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Xue, Bingxin, Cui Zhu, Xuan Wang, and Wenjun Zhu. "An Integration Model for Text Classification using Graph Convolutional Network and BERT." Journal of Physics: Conference Series 2137, no. 1 (December 1, 2021): 012052. http://dx.doi.org/10.1088/1742-6596/2137/1/012052.

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Abstract Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.

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Jha, Dipendra, Saransh Singh, Reda Al-Bahrani, Wei-keng Liao, Alok Choudhary, Marc De Graef, and Ankit Agrawal. "Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks." Microscopy and Microanalysis 24, no. 5 (October 2018): 497–502. http://dx.doi.org/10.1017/s1431927618015131.

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AbstractWe present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel. The deep learning model results in a mean disorientation error of 0.548° compared to 0.652° using dictionary based indexing.

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Journal articles on the topic 'Convolutional dictionary'

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