11 January 2017 Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery
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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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hao Liu, Kangda Li, Bing Wang, Hainie Tang, Xiaohui Gong, "Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery," Journal of Applied Remote Sensing 11(1), 015003 (11 January 2017). https://doi.org/10.1117/1.JRS.11.015003 . Submission: Received: 4 April 2016; Accepted: 22 December 2016
Received: 4 April 2016; Accepted: 22 December 2016; Published: 11 January 2017
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