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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ language:
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+ - en
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+ base_model:
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+ - prs-eth/marigold-depth-v1-0
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+ - stabilityai/stable-diffusion-2
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+ tags:
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+ - underwater restoration
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+ - latent diffusion model
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+ - image analysis
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+ - computer vision
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+ library_name: diffusers
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+ ---
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+ <h1 align="center">SLURPP Single-Step Latent Diffusion Underwater Restoration</h1>
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+
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+
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+ <p align="center">
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+ </a>
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+ <a title="Website" href="https://tianfwang.github.io/slurpp/" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%E2%99%A5%20Project%20-Website-blue" alt="Website">
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+ </a>
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+ <a title="arXiv" href="https://arxiv.org/abs/2507.07878" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/%F0%9F%93%84%20Read%20-Paper-AF3436" alt="arXiv">
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+ </a>
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+ <a title="Github" href="https://github.com/kongdai123/SLURPP" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/github/stars/kongdai123/SLURPP?label=GitHub%20%E2%98%85&logo=github&color=C8C" alt="Github">
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+
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+ <a title="License" href="https://www.apache.org/licenses/LICENSE-2.0" target="_blank" rel="noopener noreferrer" style="display: inline-block;">
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+ <img src="https://img.shields.io/badge/License-Apache--2.0-929292" alt="License">
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+ </a>
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+ </p>
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+
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+
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+ This is a model card for the `slurpp` model for underwater image restoration and water medium estimation from a single image.
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+ The model is fine-tuned from both `stable-diffusion-2` [model](https://huggingface.co/stabilityai/stable-diffusion-2) and `marigold-depth-v1-0` [model](https://huggingface.co/prs-eth/marigold-depth-v1-0).
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+
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+ This model is based on the paper:
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+
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+ Jiayi Wu, Tianfu Wang, Md Abu Bakr Siddique, Md Jahidul Islam, Cornelia Fermuller, Yiannis Aloimonos, Christopher A. Metzler "Single-Step Latent Diffusion Underwater Restoration," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025
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+
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+ ### Using the model
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+ Please follow the inference script in the [github repo](https://github.com/kongdai123/SLURPP).
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+
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+ ## Model Details
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+ - **Developed by:** [Jiayi Wu](https://jiayi-wu-leo.github.io/), [Tianfu Wang](https://tianfwang.github.io/), [Md Abu Bakr Siddique](https://www.linkedin.com/in/bbkrsddque/), [Md Jahidul Islam](https://jahid.ece.ufl.edu/), [Cornelia Fermuller](https://users.umiacs.umd.edu/~fermulcm/), [Yiannis Aloimonos](https://robotics.umd.edu/clark/faculty/350/Yiannis-Aloimonos), [Christopher A. Metzler](https://www.cs.umd.edu/people/metzler).
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+ - **Model type:** Diffusion-based single image underwater restoration and water medium prediction
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+ - **Language:** English.
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+ - **License:** [Apache License License Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
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+ - **Model Description:** Given an input underwater image, our single-step latent diffusion method SLURPP jointly predicts the clear image, and the per-pixel underwater medium parameters, specifically the backscattering and transmission parameters.
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+ - **Steps and scheduler**: Only one step is required with this model using trailing DDIM scheduler
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+ - **Outputs**:
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+ - **Clear**: The predicted clear image
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+ - **Transmission/Illuminaiton**: The ammount of attenuation experienced by each object in the scene
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+ - **Backscattering**: The amount additive light from illumination (ambient sunlight or artificial lights) that is scattered by the water volume
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+ - **Resources for more information:** [Project Website](https://tianfwang.github.io/slurpp/), [Paper](https://arxiv.org/abs/2507.0787), [Code](https://github.com/kongdai123/SLURPP).
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+ - **Cite as:**
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+
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+ ```bibtex
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+ @article{wu2025single,
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+ title={Single-Step Latent Diffusion for Underwater Image Restoration},
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+ author={Wu, Jiayi and Wang, Tianfu and Siddique, Md Abu Bakr and Islam, Md Jahidul and Fermuller, Cornelia and Aloimonos, Yiannis and Metzler, Christopher A},
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+ journal={arXiv preprint arXiv:2507.07878},
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+ year={2025}
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+ }
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+ ```