Connections Between ICA and Sparse Coding Revisited

2005 ◽  
pp. 1035-1042 ◽  
Author(s):  
Susana Hornillo-Mellado ◽  
Rubén Martín-Clemente ◽  
Juan M. Górriz-Sáez
Keyword(s):  
Sparse Coding

Image Reranking Using Multimodal Sparse Coding

2019 ◽  
Vol 7 (1) ◽  
pp. 277-282
Author(s):  
Mohammadi Aiman ◽  
Ruksar Fatima
Keyword(s):  
Sparse Coding ◽  
Image Reranking

SENSC: a Stable and Efficient Algorithm for Nonnegative Sparse Coding

2009 ◽  
Vol 35 (10) ◽  
pp. 1257-1271 ◽  
Author(s):  
Le LI ◽  
Yu-Jin ZHANG
Keyword(s):  
Sparse Coding ◽  
Efficient Algorithm

Human Action Recognition Based on Discriminative Sparse Coding Video Representation

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 745 ◽  
Author(s):  
Bin WANG ◽  
Yuanyuan WANG ◽  
Wenhua XIAO ◽  
Wei WANG ◽  
Maojun ZHANG
Keyword(s):  
Action Recognition ◽  
Sparse Coding ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Representation

Convolutional sparse coding on neurosynaptic cognitive system

2017 ◽  
Author(s):  
Md Zahangir Alom ◽  
Brian Van Essen ◽  
Adam T. Moody ◽  
David Peter Widemann ◽  
Tarek M. Taha
Keyword(s):  
Sparse Coding ◽  
Cognitive System

Energy and sparse coding coefficients as sufficient measures for VEBs classification

2021 ◽  
Vol 67 ◽  
pp. 102493
Author(s):  
Tarek Benarabi ◽  
Mourad Adnane ◽  
Moufid Mansour
Keyword(s):  
Sparse Coding

scholarly journals Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3586
Author(s):  
Wenqing Wang ◽  
Han Liu ◽  
Guo Xie
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Spectral Response ◽  
Objective Evaluation ◽  
Coefficient Matrix ◽  
Image Patch ◽  
Subjective Analysis ◽  
High Resolution Image ◽  
Sparse Coefficient

The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.

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