Blur Deconvolution and Super Resolution of Image using Unified Blind Method

Abstract

Image deblurring is the process of obtaining the original image by using the knowledge of the degrading factors. Degradation comes in many forms such as blur, noise, and camera misfocus. Blind image deconvolution is an image restoration technique that permits recovery of the target scene from a single or a set of "blurred" images in the presence of a poorly determined or unknown point spread function (PSF). It is very crucial part of image deblurring to recover image without the knowledge of the reason of its degradation. Here, an estimate is done about the unknown degradation function and using that, an estimate of the original image is produced.  This paper presents a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and Gaussian noise (AWGN). The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong, soft edges toward step edges, while filtering out weak structures. Experimental results confirm the robustness and effectiveness of the proposed method.