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Journal of Applied Remote Sensing

Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery
Author(s): Hao Liu; Kangda Li; Bing Wang; Hainie Tang; Xiaohui Gong
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Paper 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.

Paper Details

Date Published: 11 January 2017
PDF: 17 pages
J. Appl. Remote Sens. 11(1) 015003 doi: 10.1117/1.JRS.11.015003
Published in: Journal of Applied Remote Sensing Volume 11, Issue 1
Show Author Affiliations
Hao Liu, Donghua Univ. (China)
Kangda Li, Donghua Univ. (China)
Bing Wang, Donghua Univ. (China)
Hainie Tang, Donghua Univ. (China)
Xiaohui Gong, Donghua Univ. (China)

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