One-Dimensional Processing for Adaptive Image Restoration.

摘要

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 this 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. The main advantage of this new 1-D approach is its capability to preserve edges in the image while removing noise in all regions of the image, including the edge regions. To illustrate this point, the approach is applied to existing 2-D image restoration algorithms. Experimental results with images degraded by additive white noise at various SNRs (signal to noise ratios) are presented. Further examples illustrate the application of 1-D restoration techniques based on this approach to images degraded by blurring and additive white noise and images degraded by multiplicative noise. Another example shows its usefulness in the reduction of quantization noise in pulse code modulation image coding.