Aquavision Alchemy Illuminating Underwater Imagery Through Medium Transmission

Aquavision Alchemy Illuminating Underwater Imagery Through Medium Transmission

P. Sangeeta, B. Ujwal Snehith, D. Sai Kumar, B. Sahithi, A. Dheeraj

Assistant Professor Department of CSE,  Lendi Institute of Engineering & Technology; CSE,  Lendi Institute of Engineering & Technology-2,3,4,5.

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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.