Robust Kernel Estimation in Blind Deconvolution
- 10.2991/meita-15.2015.124How to use a DOI?
- Blind Deconvolution; Kernel Estimation; Normalized Sparsity Measure
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., 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.
- © 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/).
Cite this article
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 -