Adaptive Algorithm on Block-Compressive Sensing and Noisy Data Estimation

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).

A multi-hop routing algorithm for WSNs based on compressive sensing and multiple objective genetic algorithm

“…Some researches concentrate on improving the energy efficiency [20]- [23], others concentrate on improving reconstruction error [27], [28], [34]. In this work, we balance among payload, energy-efficiency, and accuracy of the reconstruction process by minimizing reconstruction error.…”

“…CS paradigm has three types of reconstruction errors, original dense data error(e θ ), sparse data error(e x ), and observed data error (e y ). They are defined as follows [34]:…”

“…where θ, x, and 4 are the sensed vectors, andθ,x, andŷ are the reconstructed vectors. Authors of [34] prove that the three errors are harmonious, and minimizing one minimizes the others.…”

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

“…Moreover, a partitioned block transform technique has been devised to mitigate the storage limitation in a remote sensing platform utilizing image coding schemes [18]. To further improve the data compression efficiency as well as CS-based recovery performance, an adaptive BCS approach has been investigated in [20,21] that uses different sub-sampling rates for individual blocks rather than using the same sub-sampling rate for all blocks. For optical images, the measurement ratio is dynamically assigned into each block according to the sparsity of wavelet coefficients [20] and the variance of sub-images [21], and each block is reconstructed by the OMP algorithm.…”

“…To further improve the data compression efficiency as well as CS-based recovery performance, an adaptive BCS approach has been investigated in [20,21] that uses different sub-sampling rates for individual blocks rather than using the same sub-sampling rate for all blocks. For optical images, the measurement ratio is dynamically assigned into each block according to the sparsity of wavelet coefficients [20] and the variance of sub-images [21], and each block is reconstructed by the OMP algorithm. These methods require a large amount of prior information about dynamic sensing matrices to continuously adjust the measurement ratio, and exhibits blocking artifacts due to the block-based CS recovery.…”