Fuzzy-Driven Image Enhancement via ABR-Fractal-Fractional Differentiation

Low-light images often suffer from low sharpness and low contrast, hindering their visual quality and the performance of computer vision systems. To address this issue, we propose a novel image enhancement algorithm that combines local detail enhancement and global contrast enhancement. Our algorithm first transforms the crisp image into an intuitionistic fuzzy image. Next, we construct a global adaptive brightness transformation membership function to generate the first enhanced image. The second enhanced image is obtained using a newly proposed fractal-fractional differential operator. Finally, an image fusion technique is employed to effectively extract image features and integrate the locally and globally enhanced images. Our proposed technique effectively enhances the quality of low-light images while preserving their unique characteristics. Experimental results demonstrate that our method achieves comparable or even superior performance compared to state-of-the-art approaches, both qualitatively and quantitatively.