Blind Deconvolution for Image Deblurring Based on Edge Enhancement and Noise Suppression

Abstract

This paper denotes to obtain an accuracy blur kernel and a shape image. An efficient method that blind deconvolution for image deblurring based on edge enhancement and noise suppression is proposed. First, we exploited an edge detection method to extract the strong edge portion of blurred image. Then, the blurred image was divided into weak edge portion and strong edge portion. At this time, we apply a trilateral filter method to suppress the noise in the weak edge portion. Through mathematical operations for weak edge portion and strong edge portion, we can obtain the new blurred image which as the input of blur kernel estimation. At the phase of the kernel estimation, the problem can be solved via alternate between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> updating. In addition, we utilize improved fast iterative shrinkage thresholding algorithm method to solve the optimization problem. Finally, non-blind deconvolution was employed at the phase of image recovery. A comprehensive evaluation shows that our approach achieves state-of-the-art results for uniformly blurred images.