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

Hao Liu; Kangda Li; Bing Wang; Hainie Tang; Xiaohui Gong

doi:10.1117/1.JRS.11.015003

11 January 2017 Entropy-aware projected Landweber reconstruction for quantized block compressive sensing of aerial imagery

Hao Liu, Kangda Li, Bing Wang, Hainie Tang, Xiaohui Gong

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 015003 (January 2017). https://doi.org/10.1117/1.JRS.11.015003

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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Hao Liu, Kangda Li, Bing Wang, Hainie Tang, and 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

Received: 4 April 2016; Accepted: 22 December 2016; Published: 11 January 2017

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KEYWORDS

Reconstruction algorithms

Quantization

Compressed sensing

Image quality

Airborne remote sensing

Denoising

Filtering (signal processing)

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