Zhou Jingchun, Wang Yanyun, Zhang Weishi, Li Chongyi
Opt Express. 2021 Aug 30;29(18):28228-28245. doi: 10.1364/OE.432900.
Underwater images frequently suffer from color casts and poor contrast, due to the absorption and scattering of light in water medium. To address these two degradation issues, we propose an underwater image restoration method based on feature priors inspired by underwater scene prior. Concretely, we first develop a robust model to estimate the background light according to feature priors of flatness, hue, and brightness, which can effectively relieve color distortion. Next, we compensate the red channel of color corrected image to revise the transmission map of it. Coupled with the structure-guided filter, the coarse transmission map is refined. The refined transmission map preserves the edge information while improving the contrast. Extensive experiments on diverse degradation scenes demonstrate that our method achieves superior performance against several state-of-the-art methods.
由于光在水介质中的吸收和散射，水下图像经常会出现偏色和对比度差的问题。为了解决这两个退化问题，我们提出了一种基于水下场景先验启发的特征先验的水下图像恢复方法。具体来说，我们首先开发了一个鲁棒模型，根据平坦度、色调和亮度的特征先验来估计背景光，这可以有效地减轻颜色失真。接下来，我们补偿颜色校正图像的红色通道以修正其透射图。结合结构引导滤波器，对粗糙的透射图进行细化。细化后的透射图在提高对比度的同时保留了边缘信息。在各种退化场景上进行的大量实验表明，我们的方法相对于几种先进方法具有卓越的性能。
