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Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation

1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
School of Electronic and Information, Suzhou University of Science and Technology, Suzhou 215009, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(7), 753; https://doi.org/10.3390/electronics8070753
Received: 14 May 2019 / Revised: 28 June 2019 / Accepted: 29 June 2019 / Published: 3 July 2019
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Abstract

In this paper, an altered adaptive algorithm on block-compressive sensing (BCS) is developed by using saliency and error analysis. A phenomenon has been observed that the performance of BCS can be improved by means of rational block and uneven sampling ratio as well as adopting error analysis in the process of reconstruction. The weighted mean information entropy is adopted as the basis for partitioning of BCS which results in a flexible block group. Furthermore, the synthetic feature (SF) based on local saliency and variance is introduced to step-less adaptive sampling that works well in distinguishing and sampling between smooth blocks and detail blocks. The error analysis method is used to estimate the optimal number of iterations in sparse reconstruction. Based on the above points, an altered adaptive block-compressive sensing algorithm with flexible partitioning and error analysis is proposed in the article. On the one hand, it provides a feasible solution for the partitioning and sampling of an image, on the other hand, it also changes the iteration stop condition of reconstruction, and then improves the quality of the reconstructed image. The experimental results verify the effectiveness of the proposed algorithm and illustrate a good improvement in the indexes of the Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), Gradient Magnitude Similarity Deviation (GMSD), and Block Effect Index (BEI). View Full-Text
Keywords: block-compressive sensing (BCS); saliency; error analysis; flexible partitioning; step-less adaptive sampling block-compressive sensing (BCS); saliency; error analysis; flexible partitioning; step-less adaptive sampling
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Zhu, Y.; Liu, W.; Shen, Q. Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation. Electronics 2019, 8, 753.

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