Search Results - "Convolutional sparse coding (CSC)"

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  1. 1

    Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing by Yao, Jing, Hong, Danfeng, Xu, Lin, Meng, Deyu, Chanussot, Jocelyn, Xu, Zongben

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 01.01.2022
    “…Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and…”
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  2. 2

    Deep High-Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification by Cheng, Chunbo, Li, Hong, Peng, Jiangtao, Cui, Wenjing, Zhang, Liming

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2022
    “… In this article, a deep high-order tensor convolutional sparse coding (CSC) model is proposed, which can be used to train deep high-order filters…”
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  3. 3

    Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning by Tolooshams, Bahareh, Dey, Sourav, Ba, Demba

    ISSN: 2162-237X, 2162-2388, 2162-2388
    Published: Piscataway IEEE 01.06.2021
    “…We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning…”
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  4. 4

    Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising by Ye, Jin, Wang, Jingran, Xiong, Fengchao, Chen, Jingzhou, Qian, Yuntao

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2025
    “…Hyperspectral images (HSIs) play a crucial role in remote sensing but are often degraded by complex noise patterns. Ensuring the physical property of the…”
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  5. 5

    PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening by Yin, Haitao

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2022
    “…Given a low-resolution multispectral (MS) image and a high-resolution panchromatic image, the task of pansharpening is to generate a high-resolution MS image…”
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  6. 6

    Seismic Multichannel Deconvolution via 2-D K-SVD and MSD-oCSC by Wang, Yaojun, Gao, Xiayu, Zhang, Guiqian, Zou, Bangli, Hu, Guangmin

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2024
    “… In this article, we present a novel data-driven multichannel seismic deconvolution method based on convolutional sparse coding (CSC) and 2-D <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula…”
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  7. 7

    Prediction of Prognosis for Nonsmall Cell Lung Cancer After Targeted Therapy Based on CT Imaging and Dense Convolutional Sparse Coding by Lu, Hua-Jun, Ma, Hai-Feng, Chen, Dao-Gui, Wei, Hang-Ping, Chen, Yong, Ying, Ting, Hua, Jia-Dong, Zhang, Da-Hai

    ISSN: 1530-437X, 1558-1748
    Published: New York IEEE 15.07.2025
    Published in IEEE sensors journal (15.07.2025)
    “… The constructed model is mainly composed of convolutional sparse coding (CSC), dense connection, and attention mechanism…”
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  8. 8

    Efficient Convolutional Sparse Coding Based on Penalized Weighted Least Squares for Seismic Data Denoising by Bai, Min, Yang, Bo, Wu, Juan, Zhou, Zixiang, Cui, Yang, Ma, Zhaoyang, Zeng, Yang

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2024
    “…Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising…”
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  9. 9

    CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation by Tang, Haitong, He, Shuang, Yang, Mengduo, Lu, Xia, Yu, Qin, Liu, Kaiyue, Yan, Hongjie, Wang, Nizhuan

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 2024
    Published in IEEE access (2024)
    “…It is still a challenging task to perform the semantic segmentation with high accuracy due to the complexity of real picture scenes. Many semantic segmentation…”
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  10. 10

    Effects of Lossy Compression on Remote Sensing Image Classification Based on Convolutional Sparse Coding by Wei, Jingru, Mi, Li, Hu, Ye, Ling, Jing, Li, Yawen, Chen, Zhenzhong

    ISSN: 1545-598X, 1558-0571
    Published: Piscataway IEEE 2022
    “…) images due to the introduced distortion by compression. In this letter, a convolutional sparse coding (CSC…”
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  11. 11

    Convolutional Sparse Coding Fast Approximation With Application to Seismic Reflectivity Estimation by Pereg, Deborah, Cohen, Israel, Vassiliou, Anthony A.

    ISSN: 0196-2892, 1558-0644
    Published: New York IEEE 2022
    “…In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building…”
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  12. 12

    A Two-Stage Convolutional Sparse Coding Network for Hyperspectral Image Classification by Cheng, Chunbo, Peng, Jiangtao, Cui, Wenjing

    ISSN: 1545-598X, 1558-0571
    Published: Piscataway IEEE 2023
    “…The convolutional sparse coding (CSC) can learn shift-invariant convolution kernels…”
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  13. 13

    A Convolutional Sparsity Regularization for Solving Inverse Scattering Problems by Song, Rencheng, Zhou, Qiao, Liu, Yu, Li, Chang, Chen, Xun

    ISSN: 1536-1225, 1548-5757
    Published: New York IEEE 01.12.2021
    “…In this letter, a convolutional sparsity regularization (CSR) is introduced into the framework of nonlinear iterative methods for solving inverse scattering…”
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  14. 14

    Piano Transcription With Convolutional Sparse Lateral Inhibition by Cogliati, Andrea, Zhiyao Duan, Wohlberg, Brendt

    ISSN: 1070-9908, 1558-2361
    Published: United States IEEE 01.04.2017
    Published in IEEE signal processing letters (01.04.2017)
    “…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…”
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  15. 15

    Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model by Wang, Lingqian, Zhou, Hui, Wang, Yufeng, Yu, Bo, Fang, Jinwei

    ISSN: 1545-598X, 1558-0571
    Published: Piscataway IEEE 01.08.2020
    Published in IEEE geoscience and remote sensing letters (01.08.2020)
    “… Considering the curse of dimensionality of conventional dictionary learning and the suboptimal solution of the patch-based dictionary learning, we take the convolutional sparse coding (CSC…”
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  16. 16

    Monaural Source Separation Using Ramanujan Subspace Dictionaries by Liao, Hsueh-Wei, Su, Li

    ISSN: 1070-9908, 1558-2361
    Published: IEEE 01.08.2018
    Published in IEEE signal processing letters (01.08.2018)
    “…Most source separation algorithms are implemented as spectrogram decomposition. In contrast, time-domain source separation is less investigated since there is…”
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  17. 17

    Slice-Based Online Convolutional Dictionary Learning by Zeng, Yijie, Chen, Jichao, Huang, Guang-Bin

    ISSN: 2168-2267, 2168-2275, 2168-2275
    Published: United States IEEE 01.10.2021
    Published in IEEE transactions on cybernetics (01.10.2021)
    “…Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While…”
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  18. 18

    A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement by Gogineni, Rajesh, Chaturvedi, Ashvini

    ISSN: 1939-1404, 2151-1535
    Published: Piscataway IEEE 01.10.2019
    “… effects and are unable to preserve the essential spatial details in the fused outcome. In this article, to overcome these drawbacks, a robust fusion scheme is proposed based on convolutional sparse coding (CSC…”
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  19. 19

    Convolutional sparse coding for demosaicking with panchromatic pixels by Bai, Chenyan, Li, Jia

    ISSN: 0923-5965, 1879-2677
    Published: Amsterdam Elsevier B.V 01.09.2019
    Published in Signal processing. Image communication (01.09.2019)
    “…Image demosaicking is the problem of reconstructing color images from raw images captured by a digital camera covered by a Color Filter Array (CFA). CFAs allow…”
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  20. 20

    Monaural Music Source Separation Using Convolutional Sparse Coding by Ping-Keng Jao, Li Su, Yi-Hsuan Yang, Wohlberg, Brendt

    ISSN: 2329-9290, 2329-9304
    Published: Piscataway IEEE 01.11.2016
    “…We present a comprehensive performance study of a new time-domain approach for estimating the components of an observed monaural audio mixture. Unlike existing…”
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