Image enhancement framework combining interval-valued intuitionistic fuzzy sets and fractional sobel operator

Abstract

Image enhancement in low-light conditions is a difficult challenge due to noise, visual impairment and colour distortion. This research describes a new method for improving low-light images utilizing fuzzy-based and fractional approach. Firstly, normalize the low-light image to minimize noise and increase clarity, resulting a fuzzy image. The fuzzy image is then turned into an intuitionistic fuzzy image (IFI), which considers membership and non-membership values, presenting a more accurate characterization of uncertainty in pixel intensities. The IFI eventually transforms into an interval-valued intuitionistic fuzzy image (IVIFI) which captures a broader range of uncertainty. A fractional Sobel mask is then used to convolute the IVIF image, resulting in an improved accurate intensity distribution. The result is an optimally enhanced image with excellent contrast and detail preservation. This proposed method gives highest values of entropy, contrast improvement index, absolute mean brightness error and colourfulness are 7.9017, 6.4658, 125.5033 and 0.2853 respectively. A comparison with existing approaches indicates the proposed method’s superiority in visual quality and quantitative metrics, emphasizing its efficacy in improving low-light images.