Robust Kernel Estimation in Blind Deconvolution

DOI

10.2991/meita-15.2015.124How to use a DOI?

Keywords

Blind Deconvolution; Kernel Estimation; Normalized Sparsity Measure

Abstract

Due to the loss of information about image and the interference of noise, blind deconvolution is an ill-posed problem. In this paper, we study this problem based on the algorithm of Krishnan et al.[1], which uses a normalized sparsity measure to solve the problem. By assuming the random high frequency property of the difference between true kernel and intermediate estimated kernel, we add a Gaussian smoothing filtering during sharp image update step. The filtering process can improve robustness of the algorithm. Experimental results show that our algorithm estimates more precise kernel and run fast than Krishnan’s original algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhiming Wang
AU  - Xing Li
PY  - 2015/08
DA  - 2015/08
TI  - Robust Kernel Estimation in Blind Deconvolution
BT  - Proceedings of the 2015 International Conference on Materials Engineering and Information Technology Applications
PB  - Atlantis Press
SP  - 682
EP  - 687
SN  - 2352-5401
UR  - https://doi.org/10.2991/meita-15.2015.124
DO  - 10.2991/meita-15.2015.124
ID  - Wang2015/08
ER  -