A Method of Image Restoration Based on Sparse Regularization
A Method of Image Restoration Based on Sparse Regularization
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DOI：
10.4028/www.scientific.net/amm.220-223.1368
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
Shu Wang;Z. Zou;Li Li-Li;X. Zhang
中科院分区：
文献类型：
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作者：
Shu Wang;Z. Zou;Li Li-Li;X. Zhang
盲解卷积是在模糊核未知的情况下，从模糊图像中恢复原始图像的过程。虽然最近的算法已经取得了巨大的进步，但在效率和稳定性方面，结果仍然远远不是完美的。为了得到稳定、唯一和有效的解，本文利用一个尺度不变的稀疏正则化函数同时对原始图像和PSF施加正则化约束。实验结果表明，本文提出的图像盲恢复算法具有较好的鲁棒性和稳定性。
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.