MDSC: Unsupervised Multipath Decomposition and Synergistic Correction for Efficient Low‐Light Image Enhancement with Detail‐Aware Smoothing

Recently, unsupervised deep learning based on the Retinex model for low‐light image enhancement (LLIE) has made significant progress. However, most existing methods typically assume gradual illumination changes and use single‐path network to first estimate illumination for image decomposition, then correct it for low‐light enhancement. This not only makes it difficult to effectively address low‐light noise and rapid illumination changes but also overlooks the potential amplification of original reflectance contrast, which limits their performance. To address these challenges, this work proposes an efficient algorithm called MDSC for LLIE, which comprises three functional modules: detail‐aware image smoothing (DAS), multipath image decomposition (MPD), and reflectance synergistic correction (RSC). Concretely, the input low‐light image is first smoothed with DAS to suppress noise while preserving details. Next, the smoothed image is processed by MPD to perform multipath directed decomposition and fusion, mitigating rapid variations in illumination and producing more accurate illumination and reflectance components. Finally, RSC jointly calibrates the illumination and reflectance components and reilluminates the scene to counteract potential amplification of reflectance contrast and produce the final enhanced result. Experimental results on challenging datasets demonstrate the effectiveness of the proposed MDSC, with both quantitative and qualitative outcomes significantly outperforming most state‐of‐the‐art approaches.