Exploration of intelligent techniques for image filtering

The soft computing techniques have been introduced in image processing in recent years and a number of promising results have been achieved. In this paper, two new techniques based on soft computing have been proposed to restore images that are degraded by blur and noise. The first technique is based on hierarchical fuzzy logic and the second is based on genetic algorithms. The hierarchical fuzzy approach is used to perform detail sharpening of an image corrupted by noise, whilst the genetic algorithm approach is used to find the optimal value of the noise-to-signal power ratio in the Wiener filter technique to restore blurred-noisy images. However, the second method can be used in a control experiment in conjunction with any image-quality-assessment measure. The measure used in this paper is the minimum mean square error (MSE) to demonstrate the effectiveness of the method.