Medium Transmission Map Matters for Learning to Restore Real-World Underwater Images
This work addresses the challenge of recognizing objects from degraded underwater images for applications like underwater exploration and archeology, representing an incremental improvement.
The paper tackles the problem of poor image restoration performance and generalization in underwater image enhancement by introducing a media transmission map as guidance, achieving a result of 22.6 dB on the challenging Test-R90 with 30 times faster speed than existing models.
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.