Abstract:
Learning-based approaches inspired by the scattering model for enhancing underwater imagery have gained prominence. Nevertheless, these methods often suffer from time-consuming attributable to their sizable model dimensions. Moreover, they face challenges in adapting unknown scenes, primarily because the scattering model’s original design was intended for atmospheric rather than marine condition. To address these obstacles, we begin by investigating the inherent differences in imaging characteristics between atmospheric and marine conditions based on statistical distributions. Building on these observations, we introduce an efficient and effective algorithm called Deep Scene Curve, abbreviated as DSC. This method comprises two essential steps: scene-irrelevant zero-mean adjustment and scene-oriented hyperparameter estimation. The first step transforms scene features into a unified zero-mean space, thereby reducing interference from scene-specific attributes. In the second step, we employ a lightweight neural network to estimate scene-oriented hyperparameters for a defined pixel-level curve based on underwater observations. This approach enables us to generate a deep curve that excels in both adaptability and efficiency, as substantiated by extensive experiments. Notably, our method achieves a significant 56% improvement in average inference time while reducing FLOPs by 92% compared to existing techniques. Furthermore, our extensive experiments in low-light image enhancement tasks highlight the potential advantages of DSC.