Adaptive deep learning framework for robust unsupervised underwater image enhancement

One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data. Underwater images are often difficult to capture and typically suffer from distortion, color loss, and reduced contrast, complicating the training of supervised deep learning models on large and diverse datasets. This limitation can adversely affect the performance of the model. In this paper, we propose an alternative approach to supervised underwater image enhancement. Specifically, we introduce a novel framework called Uncertainty Distribution Network ( UDnet ), which adapts to uncertainty distribution during its unsupervised reference map (label) generation to produce…