An Adaptive Regularized Fast Iterative Shrinkage-Thresholding Algorithm for Image Reconstruction in Compressed Sensing

Abstract:

Aiming at the problems of worse reconstructed image quality and larger time complexity of the fast iterative shrinkage-thresholding algorithm in compressed sensing, this paper presents adaptive regularized fast iterative shrinkage-thresholding algorithm. This algorithm brings in the idea of adaptively selecting regularization parameter on the basis of using gradient method and threshold shrinkage to minimize the objective function. During the iteration process regularization parameter is adaptively selected from the whole value in order to adjust the proportion of the former part and the latter part of the objective function value. Simulation results show that the proposed algorithm, compared with the traditional algorithms, obtains the better reconstructed image quality and lower time complexity.

• Periodical: Advanced Materials Research (Volume 710)
• Pages: 593-597
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.710.593
• Online since: June 2013
• Authors: Xin Meng, Shi Fang Duan, She Xiang Ma
• Keywords: Adaptive Regularization, Compressed Sensing, Fast Iterative Shrinkage-Thresholding, Image Reconstruction

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