Halo Separation-guided Underwater Multi-scale Image Restoration

TL;DR

This paper introduces a novel underwater image restoration method that effectively removes halos caused by artificial light sources, improving image quality for better underwater vision tasks.

Contribution

It proposes a halo separation-guided multi-scale restoration network with iterative structure, addressing a key challenge in underwater image enhancement under artificial lighting.

Findings

The method effectively separates halos using gradient minimization.

It improves underwater image restoration quality in scenes with artificial light.

The approach outperforms existing methods on synthetic and real datasets.

Abstract

Underwater images captured by Autonomous Underwater Vehicles (AUVs) are inevitably affected by artificial light sources, which often produce halos in the foreground of the camera and seriously interfere with the quality of the image. The existing underwater image enhancement methods fail to fully consider this key problem, and the robustness of processing images under artificial light scenes is poor. In practical applications, since underwater image enhancement itself is a very challenging task, the influence of artificial light sources will lead to serious degradation of image performance and affect subsequent vision tasks. In order to effectively deal with this problem, this paper designs a single halo image correction method based on an iterative structure. The network is mainly divided into two sub-networks, one is the halo layer separation sub-network which aims to separate the halo by gradient minimization, and the other is the multi-scale recovery sub-network which aims to recover the image information masked by halo. The UIEB and EUVP synthetic datasets are used for training to ensure that the network can fully learn the characteristics and laws of underwater halo images. Then a large number of halo images taken in an underwater environment with real artificial light are collected for testing. In addition, the brightness distribution characteristics of underwater halo images are analyzed and the radial gradient is introduced to constraint eliminate halo to improve the effect of underwater image restoration.