WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation

• URL: http://arxiv.org/abs/2510.12605v1
• Date: Tue, 14 Oct 2025 15:02:24 GMT
• Title: WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
• Authors: Runting Li, Shijie Lian, Hua Li, Yutong Li, Wenhui Wu, Sam Kwong,
• Abstract summary: We propose WaterFlow, a rectified flow-based framework for underwater salient object detection.<n>WaterFlow incorporates underwater physical imaging information as explicit priors directly into the network training process.<n>On the USOD10K dataset, WaterFlow achieves a 0.072 gain in S_m, demonstrating the effectiveness and superiority of our method.
• Score: 47.83249844545859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Underwater Salient Object Detection (USOD) faces significant challenges, including underwater image quality degradation and domain gaps. Existing methods tend to ignore the physical principles of underwater imaging or simply treat degradation phenomena in underwater images as interference factors that must be eliminated, failing to fully exploit the valuable information they contain. We propose WaterFlow, a rectified flow-based framework for underwater salient object detection that innovatively incorporates underwater physical imaging information as explicit priors directly into the network training process and introduces temporal dimension modeling, significantly enhancing the model's capability for salient object identification. On the USOD10K dataset, WaterFlow achieves a 0.072 gain in S_m, demonstrating the effectiveness and superiority of our method. The code will be published after the acceptance.

Related papers

• Learning Underwater Active Perception in Simulation [51.205673783866146]
  Turbidity can jeopardise the whole mission as it may prevent correct visual documentation of the inspected structures.<n>Previous works have introduced methods to adapt to turbidity and backscattering.<n>We propose a simple yet efficient approach to enable high-quality image acquisition of assets in a broad range of water conditions.
  arXiv  Detail & Related papers  (2025-04-23T06:48:38Z)
• Improving underwater semantic segmentation with underwater image quality attention and muti-scale aggregation attention [13.73105543582749]
  UnderWater SegFormer (UWSegFormer) is a transformer-based framework for semantic segmentation of low-quality underwater images.<n>The proposed method has advantages in terms of segmentation completeness, boundary clarity, and subjective perceptual details when compared to SOTA methods.
  arXiv  Detail & Related papers  (2025-03-30T12:47:56Z)
• PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment [59.9103803198087]
  We propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA)<n>By leveraging underwater radiative transfer theory, we integrate physics-based imaging estimations to establish quantitative metrics for these distortions.<n>The proposed model accurately predicts image quality scores and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-12-20T03:31:45Z)
• Phaseformer: Phase-based Attention Mechanism for Underwater Image Restoration and Beyond [25.975859029063585]
  We propose a lightweight phase-based transformer network with 1.77M parameters for underwater image restoration (UIR)<n>Our approach focuses on effectively extracting non-contaminated features using a phase-based self-attention mechanism.<n>We demonstrate its effectiveness for low-light image enhancement using the LOL dataset.
  arXiv  Detail & Related papers  (2024-12-02T12:48:19Z)
• FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation [65.01601309903971]
  We introduce FAFA, a Frequency-Aware Flow-Aided self-supervised framework for 6D pose estimation of unmanned underwater vehicles (UUVs) Our framework relies solely on the 3D model and RGB images, alleviating the need for any real pose annotations or other-modality data like depths. We evaluate the effectiveness of FAFA on common underwater object pose benchmarks and showcase significant performance improvements compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-09-25T03:54:01Z)
• Learning Heavily-Degraded Prior for Underwater Object Detection [59.5084433933765]
  This paper seeks transferable prior knowledge from detector-friendly images. It is based on statistical observations that, the heavily degraded regions of detector-friendly (DFUI) and underwater images have evident feature distribution gaps. Our method with higher speeds and less parameters still performs better than transformer-based detectors.
  arXiv  Detail & Related papers  (2023-08-24T12:32:46Z)
• WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond [52.27796682972484]
  Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practice. We propose a normalizing flow for detection-driven underwater image enhancement, dubbed WaterFlow. Considering the differentiability and interpretability, we incorporate the prior into the data-driven mapping procedure.
  arXiv  Detail & Related papers  (2023-08-02T04:17:35Z)
• Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN [60.257791714663725]
  We propose a Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater scenes. The proposed method outperforms the state-of-the-art supervised, semi-supervised, and unsupervised defogging approaches.
  arXiv  Detail & Related papers  (2022-12-23T03:00:28Z)
• A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline [8.326477369707122]
  This paper proposes a novel method for capturing feature information, which adds the convolutional block attention module (CBAM) to the YOLOv5 backbone. The interference of underwater creature characteristics on object characteristics is decreased, and the output of the backbone network to object information is enhanced.
  arXiv  Detail & Related papers  (2022-05-20T14:18:17Z)
• Domain Adaptive Adversarial Learning Based on Physics Model Feedback for Underwater Image Enhancement [10.143025577499039]
  We propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images. A new method for simulating underwater-like training dataset from RGB-D data by underwater image formation model is proposed. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method.
  arXiv  Detail & Related papers  (2020-02-20T07:50:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.