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A novel theoretical analysis on optimal pipeline of multi-frame image super-resolution using sparse coding

SIGNAL PROCESSING-IMAGE COMMUNICATION 2025年 130卷

作者： Afrasiabi, Mohammad Mahdi Hosseini, Reshad Abbasfar, Aliazam Univ Tehran Coll Engn Sch Elect & Comp Engn North Kargar St Tehran *** Iran

Super-resolution is the process of obtaining a high-resolution (HR) image from one or more low-resolution (LR) images. Single image super-resolution (SISR) deals with one LR image while multi-frame super-resolution (MFSR) employs several LR ones to reach the HR output. MFSR pipeline consists of alignment, fusion, and reconstruction. We conduct a theoretical analysis using sparse coding (SC) and iterative shrinkage-thresholding algorithm to fill the gap of mathematical justification in the execution order of the optimal MFSR pipeline. Our analysis recommends executing alignment and fusion before the reconstruction stage (whether through deconvolution or SISR techniques). The suggested order ensures enhanced performance in terms of peak signalto-noise ratio and structural similarity index. The optimal pipeline also reduces computational complexity compared to intuitive approaches that apply SISR to each input LR image. Also, we demonstrate the usefulness of SC in analysis of computer vision tasks such as MFSR, leveraging the sparsity assumption in natural images. Simulation results support the findings of our theoretical analysis, both quantitatively and qualitatively.

关键词： Super-resolution Single image super-resolution Theoretical analysis sparse coding Optimal pipeline

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Enhancing earth target classification in hyperspectral imagery using graph convolutional neural networks and graph-regularized sparse coding

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REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT 2025年 37卷

作者： Geetha, T. S. Chellaswamy, C. Kaliraja, T. Reddy, K. Ramachandra SA Engn Coll Dept Elect & Commun Engn Chennai 600077 India SRM TRP Engn Coll Dept Elect & Commun Engn Tiruchirappalli 621105 India SV Coll Engn Dept Elect & Commun Engn Tirupati 517502 India St Josephs Coll Engn Dept Elect & Commun Engn Chennai 600119 India

As hyperspectral remote sensing technology continues to advance, classification approaches using hyperspectral images (HSIs) have become increasingly important in earth target identification, mineral mapping, and environmental management. The strength of HSIs lies in their capacity to provide a detailed understanding of a target's composition. However, challenges such as high dimensionality, redundancy in HSI datasets, and potential class imbalances complicate their effective utilization. In this study, a novel framework combining graph convolutional neural networks (GCNNs) and graph-regularized sparse coding (GSC), referred to as GCNN-GSC, is proposed to address these challenges in HSI-based earth target classification. HSIs often exhibit irregular spatial structures, making traditional grid-based methods less effective. GCNNs excel in handling irregular grids, making them well-suited for hyperspectral data where spatial pixel arrangements deviate from regular patterns. GSC complements GCNNs by mitigating high dimensionality through compact and informative feature representation. To evaluate the efficacy of the proposed approach, a comparative study was conducted using key performance metrics, including overall accuracy, per-class accuracy, and Cohen's Kappa coefficient. The results demonstrate that GCNN-GSC outperforms state-of-the-art methods, achieving notable improvements across multiple benchmark datasets. Specifically, for the Indian Pines dataset, GCNN-GSC achieved increases of 5.74%, 5.49%, and 7.89% in Cohen's Kappa coefficient, per-class accuracy, and overall accuracy, respectively. Similar enhancements were observed for the Kennedy Space Center, Pavia University, and Houston 2013 datasets, with respective improvements of 6.58%, 6.55%, and 6.15% in Cohen's Kappa coefficient;6.24%, 6.30%, and 5.57% in per-class accuracy;and 6.24%, 6.54%, and 6.30% in overall accuracy. These results underscore the robustness and effectiveness of GCNN-GSC in hyperspectral im

关键词： sparse coding classification of hyperspectral image graph network remote sensing

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Improving the sparse coding model via hybrid Gaussian priors

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PATTERN RECOGNITION 2025年 159卷

作者： Yang, Lijian Mi, Jianxun Li, Weisheng Wang, Guofen Xiao, Bin Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China Chongqing Normal Univ Chongqing Peoples R China

sparse coding (SC) imposes a sparse prior on the representation coefficients under a dictionary or a sensing matrix. However, the sparse regularization, approximately expressed as the L1-norm, is not strongly convex. The uniqueness of the optimal solution requires the dictionary to be of low mutual coherence. Asa specialized form of SC, Convolutional sparse coding (CSC) encounters the same issue. Inspired by the Elastic Net, this paper proposes to learn an additional anisotropic Gaussian prior for the sparse codes, thus improving the convexity of the SC problem and enabling the modeling of feature correlation. Asa result, the SC problem is modified by the proposed elastic projection. We thereby analyze the effectiveness of the proposed method under the framework of LISTA and demonstrate that this simple technique has the potential to correct bad codes and reduce the error bound, especially in noisy scenarios. Furthermore, we extend this technique to the CSC model for the vision practice of image denoising. Extensive experimental results show that the learned Gaussian prior significantly improves the performance of both the SC and CSC models. Source codes are available at https://***/eeejyang/EPCSCNet.

关键词： sparse coding Denoising Deep unfolding Elastic net

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SC-VAE: sparse coding-based variational autoencoder with learned ISTA

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PATTERN RECOGNITION 2025年 161卷

作者： Xiao, Pan Qiu, Peijie Ha, Sung Min Bani, Abdalla Zhou, Shuang Sotiras, Aristeidis Washington Univ Sch Med St Louis Dept Radiol St Louis MO 63110 USA Washington Univ Sch Med St Louis Dept Radiat Oncol St Louis MO 63110 USA Washington Univ Inst Informat Data Sci & Biostat Sch Med St Louis St Louis MO 63110 USA

Learning rich data representations from unlabeled data is a key challenge towards applying deep learning algorithms in downstream tasks. Several variants of variational autoencoders (VAEs) have been proposed to learn compact data representations by encoding high-dimensional data in a lower dimensional space. Two main classes of VAEs methods may be distinguished depending on the characteristics of the meta-priors that are enforced in the representation learning step. The first class of methods derives a continuous encoding by assuming a static prior distribution in the latent space. The second class of methods learns instead a discrete latent representation using vector quantization (VQ) along with a codebook. However, both classes of methods suffer from certain challenges, which may lead to suboptimal image reconstruction results. The first class suffers from posterior collapse, whereas the second class suffers from codebook collapse. To address these challenges, we introduce a new VAE variant, termed sparse coding-based VAE with learned ISTA (SCVAE), which integrates sparse coding within variational autoencoder framework. The proposed method learns sparse data representations that consist of a linear combination of a small number of predetermined orthogonal atoms. The sparse coding problem is solved using a learnable version of the iterative shrinkage thresholding algorithm (ISTA). Experiments on two image datasets demonstrate that our model achieves improved image reconstruction results compared to state-of-the-art methods. Moreover, we demonstrate that the use of learned sparse code vectors allows us to perform downstream tasks like image generation and unsupervised image segmentation through clustering image patches. The code is available at https://***/sotiraslab/SC-VAE.

关键词： sparse coding Variational autoencoder Learned iterative shrinkage thresholding algorithm

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Convolutional sparse coding with Multipath Orthogonal Matching Pursuit

Convolutional Sparse Coding with Multipath Orthogonal Matchi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gomes, Yanis Truong, Charles Saut, Jean-Philippe Hafid, Fikri Prieur, Pascale Oudre, Laurent Université Paris Saclay Université Paris Cité ENS Paris Saclay CNRS SSA INSERM Centre Borelli Gif-sur-YvetteF-91190 France Eurobios Mews Labs CachanF-94230 France RTE Direction R&D Paris La DéfenseF-92073 France

ISBN: (纸本)9798350368741

Finding patterns in time series is crucial to understanding physical or physiological phenomena monitored with sensors. Convolutional sparse coding (CSC) methods, which approximate signals by a sparse combination of short signal templates (also called atoms), are well-suited for this task. Nevertheless, sparsity results in intractable non-convex optimization problems. This paper introduces an algorithm, based on Multipath Matching Pursuit, which is novel in convolutional settings, to efficiently and accurately estimate atoms' localizations in time series. We describe a principled way to improve this greedy procedure by returning several candidate solutions instead of one. Our approach yields better localization and signal reconstruction on simulated data and in a real-world use case, which consists in automatically detect damages, such as cracks and broken wires, in overhead power lines. © 2025 IEEE.

关键词： compressed sensing convolutional sparse coding deconvolution greedy optimization orthogonal matching pursuit sparse coding sparse representation

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sparse coding based orientation estimation for latent fingerprints

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PATTERN RECOGNITION 2017年 67卷 164-176页

作者： Liu, Shuxin Liu, Manhua Yang, Zongyuan East China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China Shanghai Jiao Tong Univ Sch EIEE Dept Instrument Sci & Engn Shanghai 200240 Peoples R China Minnan Normal Univ Sch Educ Sci Zhanghou 363000 Fujian Peoples R China

Fingerprint orientations are often used to describe the ridge flow patterns, providing useful features for further fingerprint processing and recognition. Although significant advances have been achieved for orientation estimation, it is still challenging to reliably estimate the orientations for latent fingerprints, which are usually of poor quality with unclear ridge structure and various overlapping patterns. Motivated by the recent success of sparse coding in image denoising and reconstruction, this paper proposes an orientation estimation algorithm based on dictionary learning and sparse coding for latent fingerprints. First, a texture image is obtained by decomposition of latent image with a total variation model. The structured noise is greatly reduced from the texture image. Second, we propose a multi-scale sparse coding method for iterative estimation of local ridge orientations on the texture image. Multi-scale dictionaries are learned from the orientation fields of good quality fingerprints to capture the prior knowledge of various orientation patterns, and sparse coding is iteratively applied with the increase of patch sizes to correct the corrupted orientations of latent fingerprint. The proposed algorithm can work well to reduce the effect of various noise and restore the corrupted orientations while maintain the details of singular region. Experimental results and comparisons on NIST SD27 latent fingerprint database are presented to show the effectiveness of the proposed algorithm. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Orientation estimation sparse coding Orientation field Latent fingerprint identification Biometrics

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sparse coding Driven Deep Decision Tree Ensembles for Nucleus Segmentation in Digital Pathology Images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 8088-8101页

作者： Song, Jie Xiao, Liang Molaei, Mohsen Lian, Zhichao Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Minist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China

Automating generalized nucleus segmentation has proven to he non-trivial and challenging in digital pathology. Most existing techniques in the field rely either on deep neural networks or on shallow learning-based cascading models. The former lacks theoretical understanding and tends to degrade performance when only limited amounts of training data are available while the latter often suffers from limitations for generalization. To address these issues, we propose sparse coding driven deep decision tree ensembles ((ScDTE)-T-2), an easily trained yet powerful representation learning approach with performance highly competitive to deep neural networks in the generalized nucleus segmentation task. We explore the possibility of stacking several layers based on fast convolutional sparse coding-decision tree ensemble pairwise modules and generate a layer-wise encoder-decoder architecture with intra-decoder and inter-encoder dense connectivity patterns. Under this architecture, all the encoders share the same assumption across the different layers to represent images and interact with their decoders to give fast convergence. Compared with deep neural networks, our proposed (ScDTE)-T-2 does not require back-propagation computation and depends on less hyper-parameters. (ScDTE)-T-2 is able to achieve a fast end-to-end pixel-wise training in a layer-wise manner. We demonstrated the superiority of our segmentation method by evaluating it on the multi-disease state and multi-organ dataset where consistently higher performances were obtained for comparison against other state-of-the-art deep learning techniques and cascading methods with various connectivity patterns.

关键词： Digital pathology nucleus segmentation sparse coding deep representation learning decision tree ensembles cascading strategies feature reuse

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sparse coding over redundant dictionaries for fast adaptation of speech recognition system

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COMPUTER SPEECH AND LANGUAGE 2017年 43卷 1-17页

作者： Shahnawazuddin, S. Sinha, Rohit Indian Inst Technol Guwahati Dept Elect & Elect Engn Gauhati 781039 India

This work presents a novel use of the sparse coding over redundant dictionary for fast adaptation of the acoustic models in the hidden Markov model-based automatic speech recognition (ASR) systems. The presented work is an extension of the existing acoustic model-interpolation-based fast adaptation approaches. In these methods, the basis (model) weights are estimated using an iterative procedure employing the maximum-likelihood (ML) criterion. For effective adaptation, typically a number of bases are selected and as a result of that the latency of the iterative weight estimation process becomes high for those ASR tasks that involve human-machine interactions. To address this issue, we propose the use of sparse coding of the target mean supervector over a speaker-specific (exemplar) redundant dictionary. In this approach, the employed greedy sparse coding not only selects the desired bases but also compresses them into a single supervector, which is then ML scaled to yield the adapted mean parameters. Thus reducing the latency in the basis weight estimation in comparison to the existing fast adaptation techniques. Further, to address the loss in information due to reduced degrees of freedom, we have also extended the proposed approach using separate sparse codings over multiple (exemplar and learned) redundant dictionaries. In adapting an ASR task involving human-computer interactions, the proposed approach is found to be as effective as the existing techniques but with a substantial reduction in the computational cost. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Fast adaptation Acoustic model interpolation sparse coding Exemplar and learned speaker dictionary

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sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Qi Kunlun Zhang Xiaochun Wu Baiyan Wu Huayi Wuhan Univ State Key Lab Water Resources & Hydropower Engn S 299 BaYi Rd Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R 129 Luoyu Rd Wuhan 430079 Peoples R China Hunan Univ Sci & Technol Sch Architecture & Urban Planning Taoyuan Rd Xiangtan 411201 Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol 129 Luoyu Rd Wuhan 430079 Peoples R China

High-resolution remote-sensing images are increasingly applied in land-use classification problems. Land-use scenes are often very complex and difficult to represent. Subsequently, the recognition of multiple land-cover classes is a continuing research question. We propose a classification framework based on a sparse coding-based correlaton (termed sparse correlaton) model to solve this challenge. Specifically, a general mapping strategy is presented to label visual words and generate sparse coding-based correlograms, which can exploit the spatial co-occurrences of visual words. A compact spatial representation without loss discrimination is achieved through adaptive vector quantization of correlogram in land-use scene classification. Moreover, instead of using K-means for visual word encoding in the original correlaton model, our proposed sparse correlaton model uses sparse coding to achieve lower reconstruction error. Experiments on a public ground truth image dataset of 21 land-use classes demonstrate that our sparse coding-based correlaton method can improve the performance of land-use scene classification and outperform many existing bag-of-visual-words-based methods. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： sparse coding vector quantization spatial information bag-of-visual-words

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sparse coding Neural Gas: Learning of overcomplete data representations

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NEUROCOMPUTING 2009年第7-9期72卷 1547-1555页

作者： Labusch, Kai Barth, Erhardt Martinetz, Thomas Univ Lubeck Inst Neuro & Bioinformat D-23538 Lubeck Germany

We consider the problem of learning an unknown (overcomplete) basis from data that are generated from unknown and sparse linear combinations. Introducing the sparse coding Neural Gas algorithm, we show how to employ a combination of the original Neural Gas algorithm and Oja's rule in order to learn a simple sparse code that represents each training sample by only one scaled basis vector. We generalize this algorithm by using Orthogonal Matching Pursuit in order to learn a sparse code where each training sample is represented by a linear combination of up to k basis elements. We evaluate the influence of additive noise and the coherence of the original basis on the performance with respect to the reconstruction of the original basis and compare the new method to other state of the art methods. For this analysis, we use artificial data where the original basis is known. Furthermore, we employ our method to learn an overcomplete representation for natural images and obtain an appealing set of basis functions that resemble the receptive fields of neurons in the primary visual cortex. An important result is that the algorithm converges even with a high degree of overcompleteness. A reference implementation of the methods is provided.(1) (C) 2009 Elsevier B.V. All rights reserved.

关键词： sparse coding Vector quantization Matching Pursuit Unsupervised learning

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