论文标题
l^2uwe：使用局部对比度和多尺度融合的低光水下图像有效增强的框架
L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
论文作者
论文摘要
捕获水下的图像通常会遭受次优照明设置，这些设置可以隐藏重要的视觉特征，从而降低其质量。我们提出了一种新型的单像低光的水下图像增强剂l^2uwe，这是基于我们的观察，即可以从局部对比度信息中得出有效的大气照明模型。我们创建了两个不同的模型，并从中产生了两个增强的图像：一个突出显示细节的细节，另一个重点是删除黑暗。采用多尺度融合过程来结合这些图像，同时强调更高的亮度，显着性和局部对比度的区域。我们通过使用七个指标对七种针对水下和弱光场景的最先进的增强方法进行测试来证明L^2UWE的性能。代码可在以下网址提供：https：//github.com/tunai/l2uwe。
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.
