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A novel variation-based block compressed sensing restoration method
Author(s): Huayong Yang; Xiaoli Lin
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Paper Abstract

In order to improve the performance of the current restoration method for block compressed sensing under the low complexity, we propose a novel variation-based block compressed sensing restoration method. The method decomposes the image into several non-overlapping blocks first, followed by the scanning according to the column and measurement by blocks, respectively, so as to obtain several column vectors of measurement value. The decoding end integrated the column vectors of measurement value received into matrixes, making the sparsity of image regular terms as the prior knowledge and minimizing the augmented Lagrange function as the goal. In this way, the sub-problems can be orderly solved with the variant alternating direction multiplier method, while the column vector space of image blocks was reconstructed. Finally, an anti-scanning was performed before it was combined into images. The innovation point is to apply the total variation model into the restoration framework for block compressed sensing with a small amount of calculation cost, and to extent it to a mixed variation model which can contain multiple regular terms and generality. Contrast to the current restoration algorithm relating to block compressed sensing, the simulation results show that the proposed method can achieve a better SSIM and the fastest restoration speed, while the SSIM and PSNR of the proposed method can achieve the best result.

Paper Details

Date Published: 9 August 2018
PDF: 12 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064K (9 August 2018); doi: 10.1117/12.2503371
Show Author Affiliations
Huayong Yang, Wuhan Univ. of Science and Technology (China)
Xiaoli Lin, Wuhan Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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