One-dimensional processing for adaptive image restoration

In this paper, we present a one-dimensional (1-D) approach to the problem of image restoration. Our approach involves 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 our 1-D approach is to improve the performance of the more general two-dimensional (2-D) approach. This differs considerably from previous 1-D approaches, the objectives of which have typically been to approximate a more general 2-D approach for computational reasons and not to improve its performance. To illustrate this point, our approach is applied to an existing 2-D image restoration algorithm. Experiments with images at low SNRs (signal to noise ratios) show that the 1-D approach performs better than the 2-D approach for the specific image restoration algorithm. Our 1-D approach preserves edges while removing noise in all regions of the image including the edge regions.