Computational Analysis of Different Image Super-Resolution Reconstruction Algorithms

Abstract

Super-resolution image reconstruction produces a high-resolution image from a set of shifted, blurred, and decimated versions thereof. Super-resolution image restoration has become an active research issue in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In these regularization methods, however, regularization parameter was selected by experience in some cases. Other techniques to compute the parameter had too heavy computation cost. This paper presents a generalization of restoration theory for the problem of Super-Resolution Reconstruction (SRR) of an image. In the SRR problem, a set of low quality images is given, and a single improved quality image which fuses their information is required. We present a model for this problem, and show how the classic restoration theory tools-ML, MAP and POCS-can be applied as a solution. A hybrid algorithm which joins the POCS and the ML benefits is suggested.