Designing Fuzzy Framework for Image Enrichment

Abstract

Present day applications require various kinds of images and pictures as sources of information for interpretation and analysis. Whenever an image is converted from one form to another such as, digitized, scanned, transmitted, stored, and so on, some of the degradation occurs at the output state. Hence, the output image has to undergo a process called image enrichment which consists of collection of techniques that seek to improve the visual appearance of an image. Image enhancement or enrichment basically improves the interpretability or perception of information in images for human viewers and provides better input for other automated image processing techniques. The fuzzy set theory is incorporated in this operation, in order to handle uncertainties (arising from deficiencies of information available in situations such as the dark areas of image, may be the outcome obtained from incomplete, imprecise, and not fully reliable or vague pixel information). The fuzzy logic provides a mathematical framework for representation and processing of expert knowledge that is, the Rule Base. The concept of if-then rules play a role in approximation of the variables like cross over point. Also, the uncertainties within image processing tasks are not always due to randomness but often due to vagueness and ambiguity. A fuzzy technique enables the situation to manage these problems effectively. In this thesis, Image Enhancement Fuzzy Algorithm (IEFA), a technique for image enhancement has been proposed and developed. IEFA formulates the mapping from a given input to an output using fuzzy logic. IEFA improves the contrast of low contrast images. This algorithm supports all the extension types of images. The technique begins the process of image enrichment by modifying membership functions and designing fuzzy if – then rules that exist as a sophisticated bridge between human knowledge on one side and the numerical framework of the computers on the other side. It has the capability of handling vague image data effectively. The algorithm converts image properties into fuzzy data and further, fuzzy data into crisp output through Defuzzification. Further, to evaluate the performance of the proposed technique, the developed technique has been compared with Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). It has been observed that PSNR and CII of the proposed algorithm (using a test image) are 25.56 and 1.13 respectively. These metrics are 0.078% and 6.603% effective than the metrics of existing algorithms.