Volume 7, Issue 1, February 2014, Pages 1 - 11
Motion Deblurring for Single Photograph Based on Particle Swarm Optimization
Jing Wei, Zhao Hai, Song Chunhe, Zhu Hongbo
Received 1 December 2010, Accepted 26 May 2013, Available Online 3 February 2014.
- https://doi.org/10.1080/18756891.2013.856175How to use a DOI?
- image deblurring, pose space, particle swarm optimization, latent image prediction
- This paper addresses the issue of non-uniform motion deblurring for a single photograph. The main difficulty of spatially variant motion deblurring is that, the deconvolution algorithm can not directly be used to estimate blur kernel, due to the kernel of different pixels are different with each other. In this paper we firstly build up the camera pose space, and take the blurred image as the weighted summation of all possible poses of the latent image. Then the deblurring problem is converted to searching for the optimized weighted parameters in the pose space. Due to its high dimension and non-convexity we propose a framework using the particle swarm optimization algorithm to solve the problem iteratively. We also find that regions with high frequency texture may damage the deblurring process, which motivates a new latent image prediction method. A non-linear structure tensor with anisotropic diffusion and a shock filter are combined to smooth the image while keeping the salient edges of it. Experimental results show that our approach makes it possible to model and remove non-uniform motion blur without hardware support.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Jing Wei AU - Zhao Hai AU - Song Chunhe AU - Zhu Hongbo PY - 2014 DA - 2014/02 TI - Motion Deblurring for Single Photograph Based on Particle Swarm Optimization JO - International Journal of Computational Intelligence Systems SP - 1 EP - 11 VL - 7 IS - 1 SN - 1875-6883 UR - https://doi.org/10.1080/18756891.2013.856175 DO - https://doi.org/10.1080/18756891.2013.856175 ID - Wei2014 ER -