mlsanityData to 7 Oct 2026

Marine Snow Removal Benchmarking Dataset

arXiv 2103.14249 ↗First posted 26 Mar 2021cs.CV eess.IVcited by 5

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Abstract (arXiv)

This paper introduces a new benchmarking dataset for marine snow removal of underwater images. Marine snow is one of the main degradation sources of underwater images that are caused by small particles, e.g., organic matter and sand, between the underwater scene and photosensors. We mathematically model two typical types of marine snow from the observations of real underwater images. The modeled artifacts are synthesized with underwater images to construct large-scale pairs of ground truth and degraded images to calculate objective qualities for marine snow removal and to train a deep neural network. We propose two marine snow removal tasks using the dataset and show the first benchmarking results of marine snow removal. The Marine Snow Removal Benchmarking Dataset is publicly available online.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

14 of 45 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  4. 2017A Survey on Deep Learning in Medical Image Analysiscited by 1,182
  5. 2017Deep Learning for Medical Image Analysiscited by 399
  6. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 303
  7. 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 250
  8. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  9. 2016Semantic Image Inpainting with Deep Generative Modelscited by 156
  10. 2017Deep learning in remote sensing: a reviewcited by 128
  11. 2017WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Imagescited by 112
  12. 2018Enhancing Underwater Imagery using Generative Adversarial Networkscited by 94
2 more
  1. 2019Diving Deeper into Underwater Image Enhancement: A Surveycited by 28
  2. 2018Deep Underwater Image Enhancementcited by 24

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.

5 papers.

  1. 2024PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Imagescited by 6
  2. 2023A deep learning approach for marine snow synthesis and removalcited by 2
  3. 2025Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Imagescited by 2
  4. 2026Robust Image Processing Techniques for Construction Environment Monitoring Using Underwater Robotscited by 0
  5. 2026π-SUB: A Physics-Informed Synthetic Underwater Benchmark Dataset for Underwater Image Enhancementcited by 0