Tao Ye, Dong Lili, Xu Luqiang, Xu Wenhai
Opt Express. 2021 Sep 27;29(20):32412-32438. doi: 10.1364/OE.432756.
Degradation of underwater images severely limits people to exploring and understanding underwater world, which has become a fundamental but vital issue needing to be addressed in underwater optics. In this paper, we develop an effective solution for underwater image enhancement. We first employ an adaptive-adjusted artificial multi-exposure fusion (A-AMEF) and a parameter adaptive-adjusted local color correction (PAL-CC) to generate a contrast-enhanced version and a color-corrected version from the input respectively. Then we put the contrast enhanced version into the famous guided filter to generate a smooth base-layer and a detail-information containing detail-layer. After that, we utilize the color channel transfer operation to transfer color information from the color-corrected version to the base-layer. Finally, the color-corrected base-layer and the detail-layer are added together simply to reconstruct the final enhanced output. Enhanced results obtained from the proposed solution performs better in visual quality, than those dehazed by some current techniques through our comprehensive validation both in quantitative and qualitative evaluations. In addition, this solution can be also utilized for dehazing fogged images or improving accuracy of other optical applications such as image segmentation and local feature points matching.
水下图像的退化严重限制了人们对水下世界的探索和理解，这已成为水下光学领域一个基本但至关重要且亟待解决的问题。在本文中，我们开发了一种有效的水下图像增强解决方案。我们首先采用自适应调整的人工多曝光融合（A - AMEF）和参数自适应调整的局部色彩校正（PAL - CC）分别从输入图像生成对比度增强版本和色彩校正版本。然后我们将对比度增强版本输入到著名的引导滤波器中，以生成一个平滑的基础层和一个包含细节信息的细节层。之后，我们利用颜色通道转移操作将色彩校正版本中的颜色信息转移到基础层。最后，将色彩校正后的基础层和细节层简单相加，以重建最终的增强输出。通过我们在定量和定性评估中的全面验证，所提出的解决方案得到的增强结果在视觉质量上比一些当前技术去雾后的结果表现更好。此外，该解决方案还可用于有雾图像的去雾或提高其他光学应用（如图像分割和局部特征点匹配）的准确性。
