<p>Low-light imaging presents unique challenges in capturing clear and detailed visual information. However, in practice, image deterioration, degradation, and low quality are prevalent problems that can negatively affect the ability of individuals to observe and analyze visual content accurately. Despite developing numerous image enhancement algorithms, they often exhibit high model and time complexity. This paper proposes a novel and computationally efficient approach for low-light image enhancement that addresses these limitations. The core of the method lies in utilizing the Caputo fractal fractional derivative (CFFD), offering a unique and lightweight solution. The proposed approach effectively preserves details and enhances contrast, leading to significant improvements in machine vision tasks. This paper demonstrates the proposed method’s effectiveness through qualitative and quantitative results. The performance of the proposed approach is evaluated using metrics such as entropy, peak signal noise ratio (PSNR), contrast, structural similarity index measure (SSIM), naturalness image quality evaluator (NIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The findings demonstrate that the proposed approach surpasses other algorithms in visual quality and overall performance, as measured by these metrics.</p>