论文标题
中等传输地图对于学习恢复现实世界的水下图像很重要
Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
论文作者
论文摘要
水下视觉感知对于水下探索，考古学，生态系统等基本重要。低照明，光反射，散射，吸收和悬浮颗粒不可避免地会导致急剧退化的水下图像质量，这在识别水下图像的物体时造成了巨大的挑战。现有的水下增强方法旨在促进水下可见性，严重遭受了不良的图像恢复性能和泛化能力。为了减少水下图像增强的难度，我们介绍了媒体传输图作为协助图像增强的指导。我们制定了水下视觉图像与传输图之间的相互作用，以获得更好的增强结果。即使使用简单且轻巧的网络配置，该提出的方法也可以在具有挑战性的Test-R90上获得22.6 dB的高级结果，其令人印象深刻的30倍，比现有型号快30倍。全面的实验结果表明，水下感知的优势和潜力。纸的代码提供：https：//github.com/groupg-yk/mtur-net。
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption and suspended particles inevitably lead to the critically degraded underwater image quality, which causes great challenges on recognizing the objects from the underwater images. The existing underwater enhancement methods that aim to promote the underwater visibility, heavily suffer from the poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we introduce the media transmission map as guidance to assist in image enhancement. We formulate the interaction between the underwater visual images and the transmission map to obtain better enhancement results. Even with simple and lightweight network configuration, the proposed method can achieve advanced results of 22.6 dB on the challenging Test-R90 with an impressive 30 times faster than the existing models. Comprehensive experimental results have demonstrated the superiority and potential on underwater perception. Paper's code is offered on: https://github.com/GroupG-yk/MTUR-Net.
