Abstract

Image deconvolution continues to be an active research topic of recovering a sharp image, given a blurry one generated by a convolution. One of the most challenging problems in image deconvolution is how to preserve the fifine scale texture structures while removing blur and noise. Various methods have been proposed in both spatial and transform domains, such as gradient based methods, nonlocal self-similarity methods, and sparsity based methods. However, each domain has its advantages and shortcomings, which can be complemented by each other. In this work we propose a new approach for effificient image deconvolution based on dual domain fifilters. In the deblurring process, we offer a hybrid method that a novel rolling guidance fifilter is used to ensure proper texture/structure separation, and then in the transform domain, we use the