Image-Conditional Diffusion Transformer for Underwater Image Enhancement
Quick summary
arXiv:2407.05389v2 Announce Type: replace-cross Abstract: Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering. Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT). Our method takes the degraded underwater image as the conditional input and converts it into latent space where ICDT is applied. ICDT replaces the conventional U-Net backbone in a denoising diffusion probabilistic model (DDPM) with a transformer, and thus inherits
Key takeaways
- arXiv:2407.05389v2 Announce Type: replace-cross Abstract: Underwater image enhancement (UIE) has attracted much attention owing to its importance for underwater operation and marine engineering.
- Motivated by the recent advance in generative models, we propose a novel UIE method based on image-conditional diffusion transformer (ICDT).
- Our method takes the degraded underwater image as the conditional input and converts it into latent space where ICDT is applied.
Why it matters
“Image-Conditional Diffusion Transformer for Underwater Image Enhancement” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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