Image and Signal Processing Methods

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

[+] Author Affiliations
Hao Liu, Kangda Li, Bing Wang, Hainie Tang, Xiaohui Gong

Donghua University, College of Information Science and Technology, 2999 North Renmin Road, Shanghai 201620, China

J. Appl. Remote Sens. 11(1), 015003 (Jan 11, 2017). doi:10.1117/1.JRS.11.015003
History: Received April 4, 2016; Accepted December 22, 2016
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Abstract.  A quantized block compressive sensing (QBCS) framework, which incorporates the universal measurement, quantization/inverse quantization, entropy coder/decoder, and iterative projected Landweber reconstruction, is summarized. Under the QBCS framework, this paper presents an improved reconstruction algorithm for aerial imagery, QBCS, with entropy-aware projected Landweber (QBCS-EPL), which leverages the full-image sparse transform without Wiener filter and an entropy-aware thresholding model for wavelet-domain image denoising. Through analyzing the functional relation between the soft-thresholding factors and entropy-based bitrates for different quantization methods, the proposed model can effectively remove wavelet-domain noise of bivariate shrinkage and achieve better image reconstruction quality. For the overall performance of QBCS reconstruction, experimental results demonstrate that the proposed QBCS-EPL algorithm significantly outperforms several existing algorithms. With the experiment-driven methodology, the QBCS-EPL algorithm can obtain better reconstruction quality at a relatively moderate computational cost, which makes it more desirable for aerial imagery applications.

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Citation

Hao Liu ; Kangda Li ; Bing Wang ; Hainie Tang and Xiaohui Gong
"Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery", J. Appl. Remote Sens. 11(1), 015003 (Jan 11, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.015003


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