Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images

Abstract

Low illumination, light reflections, scattering, absorption, and suspended particles inevitably lead to critically degraded underwater image quality, which poses great challenges for recognizing objects from underwater images. The existing underwater enhancement methods that aim to promote underwater visibility heavily suffer from poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we introduce the media transmission map as guidance for image enhancement. Different from the existing frameworks, which also introduce the medium transmission map for better distribution modeling, we formulate the interaction between the underwater visual images and the transmission map explicitly to obtain better enhancement results. At the same time, our network only requires supervised learning of the media transmission map during training, and the corresponding prediction map can be generated in subsequent tests, which reduces the operation difficulty of subsequent tasks. Thanks to our formulation, the proposed method with a very lightweight network configuration can produce very promising results of 22.6 dB on the challenging Test-R90 with an impressive 30.3 FPS, which is faster than most current algorithms. Comprehensive experimental results have demonstrated the superiority on underwater perception.

Keywords

• real-world underwater image enhancement
• physical prior
• deep learning