AGCSNet: High‐contrast image‐exposure correction with automatic illumination‐map attention‐based gamma and saturation correction

Abstract Low‐light image enhancement has made significant advancements in recent years. However, enhancing high‐contrast images that exhibit both under‐ and overexposure remains a major challenge. To address this issue, we propose an exposure‐correction method called AGCSNet. Two gamma corrections, and , were applied separately to correct for underexposure and overexposure, producing two gamma‐corrected images. An illumination map was used to differentiate between the underexposed and overexposed regions in the gamma‐corrected images. To mitigate the saturation anomalies caused by the gamma corrections, we introduced a new saturation‐correction method with a factor . Moreover, optimal values for , and can be predicted using our factor‐estimation deep‐learning model. We evaluated our method on eight datasets. In comparison with over 20 prior methods, our method demonstrates competitive performance with and, in some cases, surpasses state‐of‐the‐art methods that are closely aligned with human visual perception.