Image Reconstruction Based on Compressed Sensing with Split Bregman Algorithm and Fuzzy Bases

Jian Jiang Cui; Xu Jia; Jing Liu; Qi Li

doi:10.4028/www.scientific.net/AMR.508.80

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 508Image Reconstruction Based on Compressed Sensing...

Image Reconstruction Based on Compressed Sensing with Split Bregman Algorithm and Fuzzy Bases

Abstract:

When original data is not complete or image degenerates, image reconstruction and recovery will be very important. In order to acquire reconstruction or recovery image with good quality, compressed sensing provides the possibility of achieving, and an image reconstruction algorithm based on compressed sensing with split Bregman method and fuzzy bases sparse representation is proposed, split strategy is applied in split Bregman algorithm in order to accelerate convergence speed; At the same time, discrete cosine transform and dual orthogonal wavelet transform are treated as bases to represent image sparsely, and image is reconstructed by using split Bregman algorithm. Experiments show that the proposed algorithm can improve convergence speed and reconstruction image quality.

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Periodical:

Advanced Materials Research (Volume 508)

Pages:

80-83

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.508.80

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Online since:

April 2012

Authors:

Jian Jiang Cui, Xu Jia, Jing Liu, Qi Li

Keywords:

Compressed Sensing, Fuzzy Bases, Image Reconstruction, Split Bregman Algorithm

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