Výsledky vyhledávání - "convolutional sparse coding (CSC)"

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

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

“…Most hyperspectral image (HSI) data exist in the form of tensor; the tensor representation preserves the potential spatial-spectral structure information…”
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Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning Autor Tolooshams, Bahareh, Dey, Sourav, Ba, Demba

“…We introduce a neural-network architecture, termed the constrained recurrent sparse autoencoder (CRsAE), that solves convolutional dictionary learning…”
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PSCSC-Net: A Deep Coupled Convolutional Sparse Coding Network for Pansharpening Autor Yin, Haitao

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

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

“…The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data…”
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Prediction of Prognosis for Nonsmall Cell Lung Cancer After Targeted Therapy Based on CT Imaging and Dense Convolutional Sparse Coding Autor Lu, Hua-Jun, Ma, Hai-Feng, Chen, Dao-Gui, Wei, Hang-Ping, Chen, Yong, Ying, Ting, Hua, Jia-Dong, Zhang, Da-Hai

“…The prediction of prognosis for epidermal growth factor receptor (EGFR) mutation-driven nonsmall cell lung cancer (NSCLC) after targeted therapy has attracted…”
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Efficient Convolutional Sparse Coding Based on Penalized Weighted Least Squares for Seismic Data Denoising Autor Bai, Min, Yang, Bo, Wu, Juan, Zhou, Zixiang, Cui, Yang, Ma, Zhaoyang, Zeng, Yang

“…Recently, convolutional sparse coding (CSC) has been successfully applied to seismic data denoising. CSC differs from traditional dictionary learning methods…”
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CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation Autor Tang, Haitong, He, Shuang, Yang, Mengduo, Lu, Xia, Yu, Qin, Liu, Kaiyue, Yan, Hongjie, Wang, Nizhuan

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

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

“…Lossy compression causes the degradation of the classification accuracy of remote sensing (RS) images due to the introduced distortion by compression. In this…”
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A Two-Stage Convolutional Sparse Coding Network for Hyperspectral Image Classification Autor Cheng, Chunbo, Peng, Jiangtao, Cui, Wenjing

“…The convolutional sparse coding (CSC) can learn shift-invariant convolution kernels. In deep convolutional neural networks, it takes a lot of time to train the…”
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A Convolutional Sparsity Regularization for Solving Inverse Scattering Problems Autor Song, Rencheng, Zhou, Qiao, Liu, Yu, Li, Chang, Chen, Xun

“…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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Piano Transcription With Convolutional Sparse Lateral Inhibition Autor Cogliati, Andrea, Zhiyao Duan, Wohlberg, Brendt

“…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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Monaural Source Separation Using Ramanujan Subspace Dictionaries Autor Liao, Hsueh-Wei, Su, Li

“…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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Slice-Based Online Convolutional Dictionary Learning Autor Zeng, Yijie, Chen, Jichao, Huang, Guang-Bin

“…Convolutional dictionary learning (CDL) aims to learn a structured and shift-invariant dictionary to decompose signals into sparse representations. While…”
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Adaptive Seismic Single-Channel Deconvolution via Convolutional Sparse Coding Model Autor Wang, Lingqian, Zhou, Hui, Wang, Yufeng, Yu, Bo, Fang, Jinwei

“…Seismic deconvolution is a typical ill-posed inverse problem. The regularization technique in terms of different prior information is used for a unique and…”
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A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement Autor Gogineni, Rajesh, Chaturvedi, Ashvini

“…Pansharpening (PS) is a prominent remote sensing image fusion technique. It yields high-resolution multispectral (HRMS) images, which are imperative for the…”
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Convolutional sparse coding for demosaicking with panchromatic pixels Autor Bai, Chenyan, Li, Jia

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

“…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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