Deblurring text images affected by multiple kernels

Abstract

Image deblurring is one of the widely studied and challenging problems in image recovery. It is an estimation problem dealing with restoration of a linearly transformed image that is additional disturbed with noise. In our research, we propose a new method to solve deblurring problems on text images a ected by multiple kernels. In our approach we focus speci cally on almost binary images that have speci c intensity structures. First, we propose a non-convex non-blind deblurring model and provide an e cient algorithm that can restore a text-like image when the blurring kernel is known. Then we provide our alternate setting, the semi-blind problem, where a kernel is determined as a linear combination of multiple kernels. We propose how one can attack the deblurring problem by using dictionaries that are constructed using any prior information about the kernel. We propose a semi-blind deblurring model that can estimate optimal kernel using the elements of the dictionary. We consider a unique algorithm structure that favors regularizing the iterations through scaled parameter values and argue the advantages of this approach. Lastly, we consider some speci c problems that are commonly used in the literature where one can utilize our alternate problem setting. We argue how one can construct a dictionary that can maximize the utility gained by the prior information regarding the blurring process and present the performance of our model in such cases.