Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement

TL;DR

This paper introduces a novel multi-scale underwater image enhancement method using intrinsic supervision, which effectively refines details by controlling feature transmission across scales and exploiting spatial context, outperforming existing techniques.

Contribution

The proposed SMDR-IS framework uniquely integrates intrinsic supervision with multi-scale detail refinement and introduces BICA for efficient multi-scale scene information exploitation.

Findings

Outperforms state-of-the-art underwater image enhancement methods

Effectively refines multi-scale details with intrinsic supervision

Demonstrates superior restoration quality on benchmark datasets

Abstract

Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. The code is publicly available at: https://github.com/zhoujingchun03/SMDR-IS.