One-dimensional processing for adaptive image restoration

A one-dimensional (1-D) approach to the problem of adaptive image restoration is presented. In this approach, we use a cascade of four 1-D adaptive filters oriented in the four major correlation directions of the image, with each filter treating the image as a 1-D signal. The objective of developing restoration techniques using this approach is to improve the performance of two-dimensional (2-D) techniques for image restoration. This differs considerably from previous 1-D approaches, the objectives of which have typically been to approximate a 2-D approach for computational reasons and not to improve its performance. The approach is applied to existing 2-D image restoration algorithms to illustrate its main advantage, namely its capability to preserve edges in the image while removing noise in all regions of the image, including the edge regions. Experimental results with images degraded by additive white noise at various SNR´s (signal-to-noise ratios) are presented.