Multiframe blind image deconvolution using BKWV and TREG Estimators

Abstract

Accurate information from images can only be extracted if the data is free from noise, and perhaps more importantly - blur. In this study, a technique that renders a sharp version of a scene from multiple blurred frames captured over the same area, is proposed. Each image is assumed to have a different, but unknown, Point Spread Function. Kolmogorov and Moffat kernels that model the atmospheric and ionospheric effects as well as the filtering due to the lens or interferometry, are used. The problem is reduced to a series of iterations in which the blur kernel is initially estimated and subsequently used to deconvolve the input frame. The result is in turn used to update the latent image. A Block based Wavelet-Vaguelet (BKWV) method is adopted to estimate the kernel. In a second step, the algorithm makes use of Tikhonov Regularisation on the spectral domain (TREG) to compute the corresponding global estimate. Encouraging results that are comparable to those achieved by existing approaches, are obtained.