A Method of Image Restoration Based on Sparse Regularization

Abstract:

Blind deconvolution is the restoration of original image from a blurred one when the blur kernel is unknown. While recent algorithms have afforded dramatic progress, the results are still far from perfect in terms of efficiency and stability. In order to gain a stable, unique and effective solution, this paper uses a scale invariant and sparse regularization function to exert regularization constraints on the original image and PSF simultaneously. An experiment is conducted to verify that our image blind recovery algorithm is robust and has stable convergence.

• Periodical: Applied Mechanics and Materials (Volumes 220-223)
• Pages: 1368-1372
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.220-223.1368
• Online since: November 2012
• Authors: Shu Zhen Wang, Zi Jian Zou, Li Li, Xiao Ping Zhang
• Keywords: Blind Deconvolution, Image Restoration, Sparse Representation

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