Low-Light Image Enhancement via New Intuitionistic Fuzzy Generator-Based Retinex Approach

Image enhancement refers to the technique of improving an image by modifying certain aspects of its spectrum. Circumstances such as low-light and extreme darkness can lead to the loss of information, color, and quality in images. In order to address this type of situation, the current research offers an innovative approach to enhancing image quality. It employs the intuitionistic fuzzy generator (IFG) and the histogram equalization Retinex (HER) to enhance contrast in low-light and dark images, thereby maintaining color constancy. Initially, the image is fuzzified and subsequently transformed into an intuitionistic fuzzy image (IFI) by incorporating the new IFG. IFI enhances the precision of gray levels effectively by handling the uncertainty between different regions of pixels within the image. HER maintains the natural color balance in an image where fine details are more visible even in challenging lighting conditions. Experimental results demonstrate that the proposed algorithm consistently outperforms existing image enhancement methods, yielding images that maintain optimal balance in contrast level and detail using standard full referral metrics such as absolute mean brightness error, mutual information, peak signal to noise ratio, and non-referral metrics like entropy, contrast per pixel, spatial frequency, blind reference less image spatial quality evaluator, and mean intensity gradient.