A Fusion of Histogram Equalization Technique and Fuzzy Logic for Sustained Enhancement of images

Abstract

A primary method for visually representing ideas is through images, typicallycaptured using cameras. However, these captured images, whether in video or still form, oftensuffer from issues like blurriness, noise, low intensity, and differences in luminance and light intensityresulting from digitization procedure. Image enhancement becomes crucial in elevating the quality ofthese images. This paper introduces a novel approach that leverages fuzzy concepts onhistogram-equalized images for color image enhancement, facilitating a refined adjustment oflight and dark areas and thereby improving The global contrast characteristics exhibited by an image. The paper incorporates wavelet transform alongside fuzzy-enhanced histogram equalization to decompose the image into its fundamental spectral components. Further, employing image conversiontechniques allows for enhancing the image resolution effectively. The performance of thisproposed approach, surpasses the results that of the conventional histogram equalizationmethod. The evaluation metrics, including peak signal-to-noise ratio, and mean square errorestimation, indicate the superiority of the proposed approach. Experimental findings demonstrate that the proposed method effectively enhances low-contrast images, achieving not only improvements in global brightness and contrast but also the preservation of intricate details while effectively mitigating noise. Thiscomprehensive approach ensures sustained improvements in image quality, offering apromising solution for addressing common issues associated with digitized images