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   Articles with "convolutional sparse" as a keyword



Investigation on the kurtosis filter and the derivation of convolutional sparse filter for impulsive signature enhancement

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recommendations! Published in 2017 at "Journal of Sound and Vibration"

DOI: 10.1016/j.jsv.2016.10.005

Abstract: Abstract Minimum Entropy Deconvolution (MED) filter, which is a non-parametric approach for impulsive signature detection, has been widely studied recently. Although the merits of the MED filter are manifold, this method tends to over highlight… read more here.

Keywords: impulsive signature; med filter; filter; signature enhancement ... See more keywords

Two-Branch Convolutional Sparse Representation for Stereo Matching

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recommendations! Published in 2021 at "IEEE Access"

DOI: 10.1109/access.2021.3056137

Abstract: Supervised learning methods have been used to calculate the stereo matching cost in a lot of literature. These methods need to learn parameters from public datasets with ground truth disparity maps. Due to the heavy… read more here.

Keywords: stereo matching; two branch; stereo; branch convolutional ... See more keywords

Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model

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recommendations! Published in 2020 at "IEEE Geoscience and Remote Sensing Letters"

DOI: 10.1109/lgrs.2019.2945799

Abstract: Seismic deconvolution is a typical ill-posed inverse problem. The regularization technique in terms of different prior information is used for a unique and stable solution. Due to the difference between prior information and the actual… read more here.

Keywords: dictionary learning; sparse coding; csc dictionary; convolutional sparse ... See more keywords

Piano Transcription With Convolutional Sparse Lateral Inhibition

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recommendations! Published in 2017 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2017.2666183

Abstract: This letter extends our prior work on context-dependent piano transcription to estimate the length of the notes in addition to their pitch and onset. This approach employs convolutional sparse coding along with lateral inhibition constraints… read more here.

Keywords: transcription; piano transcription; piano; convolutional sparse ... See more keywords

Efficient ADMM-Based Algorithms for Convolutional Sparse Coding

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recommendations! Published in 2022 at "IEEE Signal Processing Letters"

DOI: 10.1109/lsp.2021.3135196

Abstract: Convolutional sparse coding improves on the standard sparse approximation by incorporating a global shift-invariant model. The most efficient convolutional sparse coding methods are based on the alternating direction method of multipliers and the convolution theorem.… read more here.

Keywords: admm based; sparse coding; convolutional sparse; efficient admm ... See more keywords

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration

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recommendations! Published in 2024 at "IEEE Transactions on Computational Imaging"

DOI: 10.1109/tci.2024.3393760

Abstract: Recently, complex convolutional sparse coding (ComCSC) has demonstrated its effectiveness in interferometric phase restoration, owing to its prominent performance in noise mitigation and detailed phase preservation. By incorporating the estimated coherence into ComCSC as prior… read more here.

Keywords: interferometric phase; sparse coding; coherence; convolutional sparse ... See more keywords

MSCSCformer: Multiscale Convolutional Sparse Coding-Based Transformer for Pansharpening

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recommendations! Published in 2024 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2024.3391355

Abstract: With the increasing significance of high-quality, high-resolution multispectral images (HRMSs) in various domains, pansharpening, which fuses low-resolution multispectral images (LRMSs) with high-resolution panchromatic (PAN) images, has gained considerable attention. However, current deep-learning (DL) methods have… read more here.

Keywords: resolution; transformer; sparse coding; multiscale convolutional ... See more keywords

Efficient Convolutional Sparse Coding Based on Penalized Weighted Least Squares for Seismic Data Denoising

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recommendations! Published in 2024 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2024.3459877

Abstract: Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising. CSC differs from traditional dictionary learning methods based on patching schemes in that it can directly process the whole data and capture… read more here.

Keywords: seismic data; data denoising; sparse coding; penalized weighted ... See more keywords

Fast Convolutional Sparse Dictionary Learning Based on LocOMP and Its Application to Bearing Fault Detection

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recommendations! Published in 2022 at "IEEE Transactions on Instrumentation and Measurement"

DOI: 10.1109/tim.2022.3193962

Abstract: Sparse representations based on convolutional sparse dictionary learning (CSDL) provide an excellent framework for extracting fault impulse response caused by bearing faults. In order to achieve fast dictionary learning, most CSDL-based fault diagnosis techniques recommend… read more here.

Keywords: convolutional sparse; method; fault; dictionary learning ... See more keywords

Adaptive ADMM for Dictionary Learning in Convolutional Sparse Representation

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recommendations! Published in 2019 at "IEEE Transactions on Image Processing"

DOI: 10.1109/tip.2019.2896541

Abstract: In this paper, we propose a novel approach to convolutional sparse representation with the aim of resolving the dictionary learning problem. The proposed method, referred to as the adaptive alternating direction method of multipliers (AADMM),… read more here.

Keywords: dictionary learning; admm dictionary; adaptive admm; sparse representation ... See more keywords

Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

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recommendations! Published in 2019 at "IEEE Transactions on Medical Imaging"

DOI: 10.1109/tmi.2019.2906853

Abstract: Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels… read more here.

Keywords: reconstruction; coding compressed; proposed methods; sparse coding ... See more keywords