Abstract
Traditional methods of image restoration use only one observed image for processing. In this paper, we propose methods for image restoration using several distorted images obtained with a microscanning imaging system. We assume that the observed images contain an original image degraded either by additive or by multiplicative interferences. Additionally, the images are corrupted with the additive noise of a receiver sensor. Using a set of observed images, image restoration is carried out by solving a system of equations that is derived from optimization of an objective function. Since the proposed restoration methods possess a high computational complexity, a fast algorithm is proposed. Computer simulation results obtained with the proposed methods are analyzed in terms of restoration accuracy, tolerance to the additive input noise, and robustness to sensors’position errors.
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