Adaptive Image Restoration Using Discrete Polynomial Transforms

Abstract

This paper presents a restoration algorithm based on a local signal description using discrete polynomials. The algorithm is made adaptive by estimating the local signal-to-noise ratio and by computing the corresponding deblurring filter. Furthermore, this method is developed for discrete signals, the input and output images being almost always available as discrete signals. I. Introduction Methods to describe, restore and compress signals by mean of polynomials have already been developed by Martens [1], [3] and Philips [7]. The basic idea behind these methods is the computation of filters in order to estimate the polynomial coefficients describing the ideal signal, starting from the degraded signal. Martens [3], applying these methods to image restoration assumes that each sample of the sampled degraded image corresponds to the zero-order term of the ideal image polynomial expansion. This implies that the blurring kernel is identical to the squared local window function used to desc..