Image Enhancement using Fuzzy Intensity Measure and Adaptive Clipping Histogram Equalization

TL;DR

This paper introduces FIMHE, a novel image enhancement technique combining fuzzy intensity measures with adaptive histogram clipping to improve image quality while reducing noise and distortion.

Contribution

It proposes a new method that segments and adaptively clips histograms using fuzzy measures, outperforming existing histogram equalization techniques.

Findings

FIMHE outperforms state-of-the-art methods on benchmark databases.

The method effectively reduces noise and distortion in enhanced images.

Experiments demonstrate improved visual quality and brightness preservation.

Abstract

Image enhancement aims at processing an input image so that the visual content of the output image is more pleasing or more useful for certain applications. Although histogram equalization is widely used in image enhancement due to its simplicity and effectiveness, it changes the mean brightness of the enhanced image and introduces a high level of noise and distortion. To address these problems, this paper proposes image enhancement using fuzzy intensity measure and adaptive clipping histogram equalization (FIMHE). FIMHE uses fuzzy intensity measure to first segment the histogram of the original image, and then clip the histogram adaptively in order to prevent excessive image enhancement. Experiments on the Berkeley database and CVF-UGR-Image database show that FIMHE outperforms state-of-the-art histogram equalization based methods.