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CoRe-UIE: Rethinking Coexisting and Region-wise Degradation for Underwater Image Enhancement

arXiv 2608.08965 ↗First posted 10 Aug 2026cs.AI cs.CVcited by 0

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Abstract (arXiv)

Underwater images often suffer from diverse and coexisting degradations, including color distortion, scattering haze, texture attenuation, and uneven illumination. These degradations vary across regions and may coexist locally, making conventional uniform restoration difficult to adapt to different degradation patterns. To address this problem, we propose Coexisting and Region-wise Degradation for Underwater Image Enhancement (CoRe-UIE), a degradation-oriented expert collaboration framework. CoRe-UIE combines a content-preserving shared expert with four shared-backbone routed experts for color correction, scattering suppression, texture recovery, and illumination protection. The routed experts share the same architecture but have independent parameters, and are assigned to different regions through input-derived degradation cues and region-adaptive Top-(k) routing. We further introduce a Hilbert--Schmidt Independence Criterion (HSIC)-based representation constraint to reduce statistical dependence among expert features and alleviate redundant expert responses. Experiments on UIEB, LSUI, and U45 demonstrate that CoRe-UIE achieves competitive quantitative performance and visually balanced enhancement under diverse underwater degradation conditions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 36 references that are in the map. First posted on arXiv 10 Aug 2026.

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References · 15

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  3. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
  4. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  5. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  6. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 253
  7. 2023From Sparse to Soft Mixtures of Expertscited by 248
  8. 2021Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embeddingcited by 134
  9. 2024Complexity Experts are Task-Discriminative Learners for Any Image Restorationcited by 77
  10. 2023Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bankcited by 71
  11. 2020You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Networkcited by 58
  12. 2019Diving Deeper into Underwater Image Enhancement: A Surveycited by 30
3 more
  1. 2025Iterative Predictor-Critic Code Decoding for Real-World Image Dehazingcited by 17
  2. 2021Rank-One Prior: Toward Real-Time Scene Recoverycited by 7
  3. 2024Underwater Image Enhancement with Cascaded Contrastive Learningcited by 7

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