L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
This work addresses image quality issues for underwater imaging applications, presenting an incremental improvement over existing methods.
The paper tackles the problem of low-light underwater image enhancement by proposing L^2UWE, a framework that uses local contrast and multi-scale fusion to improve image quality, demonstrating superior performance against seven state-of-the-art methods using seven metrics.
Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L^2UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information. We create two distinct models and generate two enhanced images from them: one that highlights finer details, the other focused on darkness removal. A multi-scale fusion process is employed to combine these images while emphasizing regions of higher luminance, saliency and local contrast. We demonstrate the performance of L^2UWE by using seven metrics to test it against seven state-of-the-art enhancement methods specific to underwater and low-light scenes. Code available at: https://github.com/tunai/l2uwe.