Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation
| Autor: | Qian Shen, Liu Wenbo, Yongjun Zhu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Adaptive sampling
Similarity (geometry) Computer Networks and Communications Computer science lcsh:TK7800-8360 02 engineering and technology block-compressive sensing (BCS) 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Electrical and Electronic Engineering error analysis Block (data storage) Adaptive algorithm saliency lcsh:Electronics Sampling (statistics) 020206 networking & telecommunications step-less adaptive sampling flexible partitioning Peak signal-to-noise ratio Compressed sensing Hardware and Architecture Control and Systems Engineering Feature (computer vision) Signal Processing 020201 artificial intelligence & image processing Algorithm Weighted arithmetic mean |
| Zdroj: | Electronics, Vol 8, Iss 7, p 753 (2019) Electronics Volume 8 Issue 7 |
| ISSN: | 2079-9292 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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