mlsanityData to 14 Oct 2026

Sea-ing Through Scattered Rays: Revisiting the Image Formation Model for Realistic Underwater Image Generation

arXiv 2509.15011 ↗First posted 18 Sep 2025cs.CV cs.AIcited by 2

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Abstract (arXiv)

In recent years, the underwater image formation model has found extensive use in the generation of synthetic underwater data. Although many approaches focus on scenes primarily affected by discoloration, they often overlook the model's ability to capture the complex, distance-dependent visibility loss present in highly turbid environments. In this work, we propose an improved synthetic data generation pipeline that includes the commonly omitted forward scattering term, while also considering a nonuniform medium. Additionally, we collected the BUCKET dataset under controlled turbidity conditions to acquire real turbid footage with the corresponding reference images. Our results demonstrate qualitative improvements over the reference model, particularly under increasing turbidity, with a selection rate of 82.5% by survey participants. Data and code can be accessed on the project page: vap.aau.dk/sea-ing-through-scattered-rays.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 30 references that are in the map. First posted on arXiv 18 Sep 2025.

Builds on

6 of 30 references.

References · 6

  1. 2024Depth Anything V2cited by 1,422
  2. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 737
  3. 2019Fast Underwater Image Enhancement for Improved Visual Perceptioncited by 183
  4. 2017WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Imagescited by 112
  5. 2019UWGAN: Underwater GAN for Real-world Underwater Color Restoration and Dehazingcited by 18
  6. 20253rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Resultscited by 4

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2 papers.