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이 논문의 연구 히스토리 (2)
2014
Multi-Resolution Kronecker Compressive Sensing
Thuong Nguyen Canh
,
Khanh Dinh Quoc
,
Byeungwoo Jeon
IEIE Transactions on Smart Processing & Computing
2014.02
학술저널
Texture Preserving Recovery and Multi-Resolution Sensing Matrix for Kronecker Compressive Imaging
Thuong Nguyen Canh
전자전기컴퓨터공
2014.01
학위논문
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초록· 키워드
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Compressive sensing (CS) has recently attracted considerable attention for its capability of simultaneous sampling and compression. CS produce high reconstruction performance based on the sparsity of signal in selected transform domain. However, CS still has challenges in low performance and computational complexity of reconstruction. Motivated by nonlocal structure of natural image and based on the cartoon texture image decomposition technique, this thesis proposes an efficient edges/textures preserving total variation based reconstruction algorithm. A fast implementation of the proposed method is presented using split Bregman method.
Toward more efficient sensing scheme, we study hybrid sensing matrix with combination of deterministic DCT and Gaussian random matrices which efficient sample the similarity and difference of image but broke democracy property of CS. In order to overcome this drawback and provide fast reconstruction, we further develop a novel multi-resolution KCS sensing matrix which not only provides multi-resolution measurement, reduces reconstruction running time but also improves the final reconstruction performance. The proposed scheme are evaluated via convincing numerical experiments which shows significant improvement over the conventional scheme and competitive performance with other state of the art algorithms in terms of objective and subjective quality.
Toward more efficient sensing scheme, we study hybrid sensing matrix with combination of deterministic DCT and Gaussian random matrices which efficient sample the similarity and difference of image but broke democracy property of CS. In order to overcome this drawback and provide fast reconstruction, we further develop a novel multi-resolution KCS sensing matrix which not only provides multi-resolution measurement, reduces reconstruction running time but also improves the final reconstruction performance. The proposed scheme are evaluated via convincing numerical experiments which shows significant improvement over the conventional scheme and competitive performance with other state of the art algorithms in terms of objective and subjective quality.
목차
Abstract . 9I. Introduction 11II. Background . 152.1. Compressive sensing . 152.2. Kronecker compressive sensing . 152.3. Total variation reconstruction 162.4. Nonlocal structure of natural image 18III. Total variation reconstruction for Kronecker CS 203.1. Nonlocal weighting scheme 203.1.1. Related work . 203.1.2. Edge-preserving nonlocal weighting scheme (ENOW) 223.1.3. Histogram-based ENOW Scheme . 233.2. Nonlocal regularization 253.2.1. Related work . 263.2.2. Proposed nonlocal regularization . 273.2.3. TV reconstruction with regularization and weighting scheme . 293.3. Cartoon Texture Decomposition based Reconstruction . 333.3.1. Related work . 333.3.2. Image decomposition and CS residual reconstruction 343.3.3. Decomposition based total variation reconstruction . 353.4. Decomposition based Textures preserving Reconstruction . 373.5. Experimental results . 403.5.1. Results for nonlocal weighting scheme . 413.5.2. Results for nonlocal regularization . 443.5.3. Results for decomposition based reconstruction . 473.5.4. Results for proposed texture preserving algorithm . 50IV. Multi-resolution Kronecker CS 564.1. Hybrid sensing matrix 564.1.1. Related work . 564.1.2. Hybrid DCT sensing matrix. 574.2. Multi-resolution sensing matrix . 594.2.1. Related work . 594.2.2. Block-based KCS 614.2.3. Wavelet in matrix multiplication form . 614.2.4. Multi-resolution sensing matrix 644.3. Experimental results . 66V. Conclusion 74References . 75논문요약 80
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