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IMAGE COMPRESSED SENSING BASED ON DATA-DRIVEN ADAPTIVE REDUNDANT DICTIONARIES

By Z. W. Ni, M. Zhang, J. Li, and Q. Wang

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Abstract:
Finding sparsifying transforms is an important prerequisite of compressed sensing (CS) theory. It is directly related to the reconstruction accuracy. In this work, we propose a new dictionary learning (DL) algorithm to improve the accuracy of CS reconstruction. In the proposed algorithm, Least Angle Regression (LARS) algorithm and an approximate SVD method (ASVD) are respectively used in the two stages. In addition, adaptive sparsity constraint is used in the sparse representation stage, to obtain sparser representation coefficient, which further improves the dictionary update stage. With these data-driven adaptive dictionaries as sparsifying transforms for image compressed sensing, results of experiments demonstrate noteworthy outperformance in peak signal-to-noisy ratio (PSNR), compared to CS based on dictionaries learned by K-SVD, in the sampling rate of 0.2-0.5. Besides, visual appearance of reconstruction detail at low sampling rate improves, for reducing of `block' effect.

Citation:
Z. W. Ni, M. Zhang, J. Li, and Q. Wang, "Image Compressed Sensing Based on Data-Driven Adaptive Redundant Dictionaries," Progress In Electromagnetics Research M, Vol. 22, 73-89, 2012.
doi:10.2528/PIERM11093004

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