INDEPENDENT COMPONENT ANALYSIS FOR BLIND IMAGE DECONVOLUTION AND DEBLURRING

Blind deconvolution or deblurring is a challenging problem in image processing applications as images often suffer from blurring or point spreading with unknown blurring kernels or point-spread functions as well as noise corruption. Most existing methods require certain knowledge about both the image and the kernel and their performance much depends on the amount of prior information regarding the both. Independent component analysis (ICA) has emerged as a useful method for recovering signals from their mixtures. However, ICA usually requires a number of different input signals to uncover the unknown mixing mechanism. In this paper a recently proposed blind deconvolution and deblurring method (Yin and Hussain, 2008) is described. It is based on the nongaussianity measure of ICA as well as a genetic algorithm. The method is simple and requires little prior knowledge regarding the blurring process, but is able to estimate or approximate the blurring kernel from a single blurred image. Various blurring functions are described and the proposed method has been tested on images degraded by various blurring kernels and the results are compared to those of existing methods such as Wiener filter, regularization filter, and the Richardson-Lucy method. Experimental results show that the proposed method outperforms these methods and can be a, useful approach to wider imaging applications