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

TL;DR

This paper introduces the use of a media transmission map as guidance in a lightweight network to significantly improve underwater image restoration, achieving superior quality and speed over existing methods.

Contribution

It proposes a novel transmission map-guided approach for underwater image enhancement, enhancing restoration quality and efficiency with a simple network architecture.

Findings

Achieves 22.6 dB on Test-R90 dataset.

30 times faster than existing models.

Demonstrates superior generalization and perception improvements.

Abstract

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.