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access icon free Sparsity-aware adaptive block-based compressive sensing

© The Institution of Engineering and Technology
Received 29/03/2016, Accepted 21/07/2016, Revised 23/06/2016, Published 22/07/2016

Conventional methods for block-based compressive sensing consider an equal number of samples for all blocks. However, the sparsity order of blocks in natural images could be different and, therefore, a various number of samples could be required for their reconstruction. In this study, the authors propose an adaptive block-based compressive sensing scheme, which collects a different number of samples from each block. The authors show that by adapting the sampling rate, in addition to reducing the whole required number of measurements, the reconstruction performance would be improved, simultaneously. Simulation results verify the effectiveness of the proposed scheme, especially for multi-level pixel value images like Mondrian test image.

Inspec keywords: compressed sensing; image reconstruction

Other keywords: natural images; block sparsity order; sampling rate; image reconstruction; sparsity-aware adaptive compressive sensing; adaptive block-based compressive sensing scheme; multilevel pixel value images

Subjects: Optical, image and video signal processing; Computer vision and image processing techniques

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