Degradation-Aware Multi-Stage Fusion for Underwater Image Enhancement
Lian Xie, Hao Chen, Jin Shu
TL;DR
This paper introduces a two-stage framework for real-time underwater image enhancement that classifies and corrects common image degradations efficiently.
Contribution
A modular, degradation-aware framework with lightweight CNN classification and adaptive fusion modules for real-time underwater image enhancement.
Findings
Stage I classifies underwater image degradations with 91.85% accuracy.
Linear Fusion improves PSNR by +2.6 dB and perceptual metrics by ~20.7%.
LiteUNetFusion further improves PSNR by +1.5 dB and preserves texture and color consistency.
Abstract
Underwater images frequently suffer from color casts, low illumination, and blur due to wavelength-dependent absorption and scattering. We present a practical two-stage, modular, and degradation-aware framework designed for real-time enhancement, prioritizing deployability on edge devices. Stage I employs a lightweight CNN to classify inputs into three dominant degradation classes (color cast, low light, blur) with 91.85% accuracy on an EUVP subset. Stage II applies three scene-specific lightweight enhancement pipelines and fuses their outputs using two alternative learnable modules: a global Linear Fusion and a LiteUNetFusion (spatially adaptive weighting with optional residual correction). Compared to the three single-scene optimizers (average PSNR = 19.0 dB; mean UCIQE ≈ 0.597; mean UIQM ≈ 2.07), the Linear Fusion improves PSNR by +2.6 dB on average and yields roughly +20.7% in UCIQE…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Image Processing Techniques · Generative Adversarial Networks and Image Synthesis