PSO Based Motion Deblurring for Single Image

This paper addresses the issue of non-uniform motion deblurring due to hand shake for a single photograph. The main difficulty of spatially variant motion deblurring is that the deconvolution algorithm can not directly be used to estimate the blur kernel as the kernel of different pixels are different to each other. In this paper, the blurred image is considered as a weighted summation of all possible poses, and we proposed to use a PSO (particle swarm optimization) to optimize the weighed parameters of the corresponding poses after building the motion model of the camera, and an alternatively optimizing procedure is used to gradually refine the motion kernel and the latent image. The main issue of using a PSO for deblurring is that it is generally impossible to obtain the ground true of the observed blurred image, which must be used as the input of the PSO algorithm. In this paper, a non-linear structure tensor with anisotropic diffusion is used to smooth the texture while keeping the salient edges in the image. Experimental results show the validly of the algorithm.