A fuzzy operator for the enhancement of blurred and noisy images

Abstract

Rule-based fuzzy operators are a novel class of operators specifically designed in order to apply the principles of approximate reasoning to digital image processing. This paper shows how a fuzzy operator that is able to perform detail sharpening but is insensitive to noise can be designed. The results obtainable by the proposed technique in the enhancement of a real image are presented.

Nonlinear Rule-based Convolution for Refocusing

“…Sharpening makes a pixel that is dissimilar to its neighbors even more dissimilar [12,15,16], which we do only if the dierence is signi®cant. This is the function of the beetle.…”

“…The goal is to sharpen without boosting the power of low energy impulse noise rather than the removal of noise, but our method actually removes some low level impulse noise. The knowledge embodied in the rules was developed by Russo and Ramponi in [12] and we conceptualize it via an exploratory constructor beetle. We are able to use one or two rules to replace a few dozen of their rules.…”

The important properties and applications of the adaptive weighted fuzzy mean filter

“…Russo and Ramponi 14 applied heuristic knowledge to build fuzzy rule based operators for smoothing, sharpening, and edge detection. They can perform smoothing noise efficiently and preserving edges well, but the common drawback of these methods is that they are sensitive to impulse noise when the noise rate is over 50%.…”

Multistage Sub-Image Filtering Technique with Improved De-noising Feature for Road Detection in Disaster Management

“…Several fuzzy filters for noise reduction have been developed, e.g., the well-known FIRE-filter [35], the weighted fuzzy mean filter [36]. The adaptive weighted fuzzy mean filter [37] (AWFM), the histogram adaptive fuzzy filter [38] (HAF) and the adaptive fuzzy switching filter (AFSF) which uses the maximum-minimum exclusive median filter [39], are other examples of the state-of-the-art methods.…”