Motion Deblurring for Single Photograph Based on Particle Swarm Optimization

DOI

10.1080/18756891.2013.856175How to use a DOI?

Keywords

image deblurring, pose space, particle swarm optimization, latent image prediction

Abstract

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.

Copyright

© 2017, 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  - JOUR
AU  - Jing Wei
AU  - Zhao Hai
AU  - Song Chunhe
AU  - Zhu Hongbo
PY  - 2014
DA  - 2014/02/03
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  - 10.1080/18756891.2013.856175
ID  - Wei2014
ER  -