Deblurring of grayscale images using inverse and Wiener filter

Abstract

Images are produced to record or display useful information. Due to imperfections in the imaging and capturing process, however, the recorded image invariably represents a degraded version of the original scene. The field of image restoration (sometimes referred to as image deblurring or image deconvolution) is concerned with the reconstruction or estimation of the uncorrupted image from a blurred and noisy one. The purpose of image restoration is to produce best estimate of source image, given the recorded data and some apriori knowledge. In this paper, technique is presented which attempts to use two algorithms for image restorations: Wiener filter and Fourier inverse filter including further work as implementation of Lucy Richardson algorithm. Inverse filtering is the process of recovering the degraded image. Inverse filters are useful for precorrecting an input signal in anticipation of the degradations caused by the system. This approach also suffers from problems that in most cases produce unacceptable results, assumes no noise, only blurring. The preferred approach is therefore to use methods based on least squares. The so-called Wiener filter is the classic solution to the problem of minimizing the mean squared restoration error, the difference between the original and restored images.