Summary of Image-conditional Diffusion Transformer For Underwater Image Enhancement, by Xingyang Nie et al.
Image-Conditional Diffusion Transformer for Underwater Image Enhancement
by Xingyang Nie, Su Pan, Xiaoyu Zhai, Shifei Tao, Fengzhong Qu, Biao Wang, Huilin Ge, Guojie Xiao
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel underwater image enhancement (UIE) method is proposed based on the image-conditional diffusion transformer (ICDT). This approach combines generative models with a transformer architecture to enhance degraded underwater images. The ICDT model takes the input image as a conditional input and converts it into latent space where the denoising diffusion probabilistic model (DDPM) is applied. A hybrid loss function involving variances is used to accelerate the sampling process while achieving better log-likelihoods. Experimental results demonstrate the scalability of ICDTs and show that the largest model, ICDT-XL/2, outperforms previous methods on the Underwater ImageNet dataset, achieving state-of-the-art (SOTA) quality in image enhancement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve underwater images is being explored. This method uses a special kind of artificial intelligence called a “transformer” to make blurry or noisy underwater pictures clearer and more like real life. The researchers tested their approach on many different types of underwater images and found that it worked really well, even with very large and complex images. This could be important for people who work underwater, like engineers or scientists. |
Keywords
» Artificial intelligence » Diffusion » Latent space » Loss function » Probabilistic model » Transformer
