Spatially non-uniform blur analysis based on wavelet transform

Object motion causes spatially varying blur in an image. Partial blur typically carries useful information about the scene. This information is useful for consumer imaging as well as computer vision. However, spatially varying blur also deteriorates image quality. The goals of our research are finding out this information and making images better. In this thesis we introduce a novel method for solving this partial blur problem. We define a statistical model of a spatially-varying blur image and estimate the local point spread function (PSF) by using a set of methods including double wavelet transform and local autocorrelation. Experimental results demonstrate the effectiveness of the proposed algorithm