Aquavision Alchemy Illuminating Underwater Imagery Through Medium Transmission

Abstract

Restoring underwater scenes often involves addressing interference caused by the underwater environment. Many existing methods overlook the scale-related characteristics inherent in these scenes. To tackle this, we propose a synergistic multi-scale detail refinement method for enhancing underwater scene details. This approach comprises multiple stages, starting with a low-degradation stage that enriches original images with multi-scale details using the Adaptive Selective Intrinsic Supervised Feature module. ASISF, through intrinsic supervision, precisely manages feature transmission across different degradation stages, refining multi-scale details while minimizing irrelevant information. Within the framework, we introduce the Bifocal Intrinsic-Context Attention Module, which efficiently utilizes multi-scale scene information by leveraging spatial contextual relationships. Throughout training, a multi-degradation loss function enhances the network's ability to extract information across various scales. The proposed method consistently outperforms state-of-the-art methods when evaluated against them. Key Words: Interference mitigation, Multi-scale detail refinement, Intrinsic supervision, Spatial contextual relationships.