Journal articles: 'Convolutional Dictionary Learning (CDL)'

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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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Turquais, Pierre, Endrias G. Asgedom, and Walter Söllner. "A method of combining coherence-constrained sparse coding and dictionary learning for denoising." GEOPHYSICS 82, no. 3 (May 1, 2017): V137—V148. http://dx.doi.org/10.1190/geo2016-0164.1.

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We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.

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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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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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Wan, Fucheng, Dengyun Zhu, Xiangzhen He, Qi Guo, Dongjiao Zhang, Zhenyang Ren, and Yuxiang Du. "Agricultural Product Recommendation Model based on BMF." Applied Mathematics and Nonlinear Sciences 5, no. 2 (July 1, 2020): 415–24. http://dx.doi.org/10.2478/amns.2020.2.00060.

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Abstract In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.

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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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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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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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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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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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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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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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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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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, 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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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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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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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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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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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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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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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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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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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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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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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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Farmonov, Nizom, Khilola Amankulova, Shahid Nawaz Khan, Mokhigul Abdurakhimova, József Szatmári, Tukhtaeva Khabiba, Radjabova Makhliyo, Meiliyeva Khodicha, and László Mucsi. "Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting." Hungarian Geographical Bulletin 72, no. 4 (January 12, 2024): 383–98. http://dx.doi.org/10.15201/hungeobull.72.4.4.

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Crop yield forecasting is critical in modern agriculture to ensure food security, economic stability, and effective resource management. The main goal of this study was to combine historical multisource satellite and environmental datasets with a deep learning (DL) model for soybean yield forecasting in the United States’ Corn Belt. The following Moderate Resolution Imaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer (CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected. Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: random forest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting (XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neural network (1D-CNN). The best model was determined by comparing their performances at forecasting the soybean yield in 2017–2021 at the county scale. The RF model outperformed all other ML models with the lowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452 t/ha) regression. However, the 1D-CNN model showed the highest forecasting accuracy for the 2018 growing season with RMSE of 0.280 t/ha. The developed 1D-CNN model has great potential for crop yield forecasting because it effectively captures temporal dependencies and extracts meaningful input features from sequential data.

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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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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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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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Zhang, Xuesong, Baoping Li, and Jing Jiang. "Hessian Free Convolutional Dictionary Learning for Hyperspectral Imagery With Application to Compressive Chromo-Tomography." IEEE Access 8 (2020): 104216–31. http://dx.doi.org/10.1109/access.2020.2999457.

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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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Öztürk, Şaban. "Convolutional neural network based dictionary learning to create hash codes for content-based image retrieval." Procedia Computer Science 183 (2021): 624–29. http://dx.doi.org/10.1016/j.procs.2021.02.106.

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Ben-Cohen, Avi, Eyal Klang, Ariel Kerpel, Eli Konen, Michal Marianne Amitai, and Hayit Greenspan. "Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations." Neurocomputing 275 (January 2018): 1585–94. http://dx.doi.org/10.1016/j.neucom.2017.10.001.

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Kang, Yanqin, Jin Liu, Fan Wu, Kun Wang, Jun Qiang, Dianlin Hu, and Yikun Zhang. "Deep convolutional dictionary learning network for sparse view CT reconstruction with a group sparse prior." Computer Methods and Programs in Biomedicine 244 (February 2024): 108010. http://dx.doi.org/10.1016/j.cmpb.2024.108010.

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Guo, Quanyi. "Occluded Face Recognition based on Deep Learning." Frontiers in Computing and Intelligent Systems 5, no. 2 (September 1, 2023): 120–23. http://dx.doi.org/10.54097/fcis.v5i2.13134.

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Compared to the traditional sparse representation and the dictionary processing method of occlusion, deep learning-based face recognition methods are being used more and more widely in the field of face recognition. However, in practice, face recognition results are greatly influenced by light intensity, shooting Angle, mask and sunglasses occlusion and other factors. Therefore, this paper will discuss the face recognition under the occlusion situation. In order to solve the problem of large pose change of human face and local occlusion respectively, an offset network and a weight network was introduced into the convolutional neural network. In the following paper, the facial recognition accuracy of the introduction of the offset network, the facial recognition accuracy of the weight network and the recognition accuracy of the unification of the two are compared with the traditional facial recognition model VGG16.

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Gu, Binjie, Weili Xiong, and Zhonghu Bai. "Human Action Recognition Based on Supervised Class-specific Dictionary Learning with Deep Convolutional Neural Network Features." Computers, Materials & Continua 62, no. 3 (2020): 243–62. http://dx.doi.org/10.32604/cmc.2020.06898.

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Dong, Qunxi, Jie Zhang, Qingyang Li, Junwen Wang, Natasha Leporé, Paul M. Thompson, Richard J. Caselli, Jieping Ye, and Yalin Wang. "Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images." Journal of Alzheimer's Disease 75, no. 3 (June 2, 2020): 971–92. http://dx.doi.org/10.3233/jad-190973.

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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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Młynarski, Wiktor, and Josh H. McDermott. "Learning Midlevel Auditory Codes from Natural Sound Statistics." Neural Computation 30, no. 3 (March 2018): 631–69. http://dx.doi.org/10.1162/neco_a_01048.

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Interaction with the world requires an organism to transform sensory signals into representations in which behaviorally meaningful properties of the environment are made explicit. These representations are derived through cascades of neuronal processing stages in which neurons at each stage recode the output of preceding stages. Explanations of sensory coding may thus involve understanding how low-level patterns are combined into more complex structures. To gain insight into such midlevel representations for sound, we designed a hierarchical generative model of natural sounds that learns combinations of spectrotemporal features from natural stimulus statistics. In the first layer, the model forms a sparse convolutional code of spectrograms using a dictionary of learned spectrotemporal kernels. To generalize from specific kernel activation patterns, the second layer encodes patterns of time-varying magnitude of multiple first-layer coefficients. When trained on corpora of speech and environmental sounds, some second-layer units learned to group similar spectrotemporal features. Others instantiate opponency between distinct sets of features. Such groupings might be instantiated by neurons in the auditory cortex, providing a hypothesis for midlevel neuronal computation.

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Wang, Pengxin, Liuyang Song, Yansong Hao, Huaqing Wang, Shi Li, and Lingli Cui. "A light intelligent diagnosis model based on improved Online Dictionary Learning sample-making and simplified convolutional neural network." Measurement 183 (October 2021): 109813. http://dx.doi.org/10.1016/j.measurement.2021.109813.

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Nguyen, Franck, Selim M. Barhli, Daniel Pino Muñoz, and David Ryckelynck. "Computer Vision with Error Estimation for Reduced Order Modeling of Macroscopic Mechanical Tests." Complexity 2018 (December 2, 2018): 1–10. http://dx.doi.org/10.1155/2018/3791543.

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In this paper, computer vision enables recommending a reduced order model for fast stress prediction according to various possible loading environments. This approach is applied on a macroscopic part by using a digital image of a mechanical test. We propose a hybrid approach that simultaneously exploits a data-driven model and a physics-based model, in mechanics of materials. During a machine learning stage, a classification of possible reduced order models is obtained through a clustering of loading environments by using simulation data. The recognition of the suitable reduced order model is performed via a convolutional neural network (CNN) applied to a digital image of the mechanical test. The CNN recommend a convenient mechanical model available in a dictionary of reduced order models. The output of the convolutional neural network being a model, an error estimator, is proposed to assess the accuracy of this output. This article details simple algorithmic choices that allowed a realistic mechanical modeling via computer vision.

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Zhou, Qing, Zuren Feng, and Emmanouil Benetos. "Adaptive Noise Reduction for Sound Event Detection Using Subband-Weighted NMF." Sensors 19, no. 14 (July 20, 2019): 3206. http://dx.doi.org/10.3390/s19143206.

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Sound event detection in real-world environments suffers from the interference of non-stationary and time-varying noise. This paper presents an adaptive noise reduction method for sound event detection based on non-negative matrix factorization (NMF). First, a scheme for noise dictionary learning from the input noisy signal is employed by the technique of robust NMF, which supports adaptation to noise variations. The estimated noise dictionary is used to develop a supervised source separation framework in combination with a pre-trained event dictionary. Second, to improve the separation quality, we extend the basic NMF model to a weighted form, with the aim of varying the relative importance of the different components when separating a target sound event from noise. With properly designed weights, the separation process is forced to rely more on those dominant event components, whereas the noise gets greatly suppressed. The proposed method is evaluated on a dataset of the rare sound event detection task of the DCASE 2017 challenge, and achieves comparable results to the top-ranking system based on convolutional recurrent neural networks (CRNNs). The proposed weighted NMF method shows an excellent noise reduction ability, and achieves an improvement of an F-score by 5%, compared to the unweighted approach.

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Hong, Jia-Sheng, Ingo Hermann, Frank Gerrit Zöllner, Lothar R. Schad, Shuu-Jiun Wang, Wei-Kai Lee, Yung-Lin Chen, Yu Chang, and Yu-Te Wu. "Acceleration of Magnetic Resonance Fingerprinting Reconstruction Using Denoising and Self-Attention Pyramidal Convolutional Neural Network." Sensors 22, no. 3 (February 7, 2022): 1260. http://dx.doi.org/10.3390/s22031260.

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Magnetic resonance fingerprinting (MRF) based on echo-planar imaging (EPI) enables whole-brain imaging to rapidly obtain T1 and T2* relaxation time maps. Reconstructing parametric maps from the MRF scanned baselines by the inner-product method is computationally expensive. We aimed to accelerate the reconstruction of parametric maps for MRF-EPI by using a deep learning model. The proposed approach uses a two-stage model that first eliminates noise and then regresses the parametric maps. Parametric maps obtained by dictionary matching were used as a reference and compared with the prediction results of the two-stage model. MRF-EPI scans were collected from 32 subjects. The signal-to-noise ratio increased significantly after the noise removal by the denoising model. For prediction with scans in the testing dataset, the mean absolute percentage errors between the standard and the final two-stage model were 3.1%, 3.2%, and 1.9% for T1, and 2.6%, 2.3%, and 2.8% for T2* in gray matter, white matter, and lesion locations, respectively. Our proposed two-stage deep learning model can effectively remove noise and accurately reconstruct MRF-EPI parametric maps, increasing the speed of reconstruction and reducing the storage space required by dictionaries.

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Li, Chen, and Fanfan Li. "Emotion recognition of social media users based on deep learning." PeerJ Computer Science 9 (June 14, 2023): e1414. http://dx.doi.org/10.7717/peerj-cs.1414.

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Issues with sentiment analysis in social media include neglecting the long-distance semantic link of emotional features, failing to capture the feature words with emotional hue effectively, and depending excessively on manual annotation. This research provides a user emotion recognition model to achieve the emotional analysis of microblog public opinion events. Three types of inspiring text, “joy,” “anger,” and “sadness,” are obtained by the data collecting and data preprocessing of micro-blog public opinion event comment text. Then, an algorithm using the linear discriminant analysis (LDA) model, emotion dictionary, and manual annotation is created to extract emotional feature words. The captured motivational text is converted into a word vector using Word2vec. After gathering the long-distance semantic data with bidirectional long short-term memories (BiLSTM) and convolutional neural networks (CNN) extract the text’s key characteristics to finish the emotion categorization. The test results demonstrate an average increase in F1 value of 3.66 percent for six machine learning models and an average increase in F1 value of 1.84 percent for seven deep learning models. The suggested model performs better at identifying the emotions of social media users than the current machine learning and deep learning methods.

Journal articles on the topic 'Convolutional Dictionary Learning (CDL)'

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