<p>Capturing clear and detailed visual information in low-light conditions poses unique challenges. Despite the numerous low-light image enhancement (IE) algorithms available, very few specifically address IE based on the image’s contrast, brightness, and visibility (CBV) levels. Moreover, these methods often fail to significantly improve the overall visual quality and reveal intricate patterns of low-light photos, leading to overexposed or underexposed results. To tackle this issue, an adaptive IE approach is introduced, which enhances low-light images by adjusting their CBV levels, thereby achieving balanced and visually pleasing enhancements. Initially, the algorithm transforms the crisp image into an intuitionistic fuzzy image. Subsequently, an innovative intuitionistic fuzzy generator (IFG) is employed for global IE to obtain the first enhanced image. Secondly, the fractal-fractional derivative (FFD) is applied for local IE to produce the second enhanced image. An image fusion technique integrates locally and globally enhanced images and extracts their features. The effectiveness of the proposed architecture is showcased through both qualitative and quantitative outcomes. Performance evaluations are conducted using metrics such as entropy, peak signal-to-noise ratio (PSNR), contrast, and blind/referenceless image spatial quality evaluator (BRISQUE). The outcome of evaluations indicates that the proposed approach outperforms existing algorithms in terms of visual quality and overall performance, as assessed by the above metrics. The proposed method is evaluated using LoL, MEF, DICM, LIME, NPE, and VV datasets. Specifically, it shows average improvements of 2.37% in entropy, 33.90% in PSNR, a remarkable 92% in contrast, and 3.17% in BRISQUE, outperforming the existing method [<CitationRef CitationID="CR44">44</CitationRef>].</p>