Application of Blind Deconvolution Algorithm for Image Restoration

摘要

Image restoration is the process of recovering the original image from the degraded image and also understand the image without any artifacts errors. Image restoration methods can be considered as direct techniques when their results are produced in a simple one step fashion. Equivalently, indirect techniques can be considered as those in which restoration results are obtained after a number of iterations. Known restoration techniques such as inverse filtering and Wiener Filtering can be considered as simple direct restoration techniques. The problem with such methods is that they require knowledge of the blur function that is point-spread function (PSF), which is, unfortunately, usually not available when dealing with image blurring . In this paper Blind deconvolution for image restoration is discussed which is the recovery of a sharp version of a blurred image when the blur kernel is unknown. The fundamental task of image deblurring is to de-convolute the blurred/degraded image with the PSF that exactly describes the distortion. Firstly, the original image is degraded using the Degradation Model. It can be done by Gaussian Filter which is low-pass filter used to blur in image. In the edges of degraded image, the ringing effect due to high frequency drop-off can be detected using Canny Edge detection methods. This ringing effect should be removed before restoration using edge trapping. After removing the ringing effect, blind Deconvolution algorithm is applied to the blurred images. It is possible to renovate the original image without having specific knowledge of degradation filter, additive noise and image spectral density. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally.