✨One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models

📝 Summary:
LUA performs efficient super-resolution directly in diffusion models' latent space. This lightweight module enables faster, high-quality image synthesis by upscaling before VAE decoding, cutting time versus pixel-space methods, and generalizing across VAEs.

🔹 Publication Date: Published on Nov 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.10629
• PDF: https://arxiv.org/pdf/2511.10629

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#DiffusionModels #SuperResolution #LatentSpace #ImageGeneration #AIResearch

✨MRI Super-Resolution with Deep Learning: A Comprehensive Survey

📝 Summary:
This survey comprehensively reviews deep learning methods for MRI super-resolution, enabling high-resolution imaging from low-resolution scans. It categorizes techniques, discusses challenges, and provides valuable resources for the community.

🔹 Publication Date: Published on Nov 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16854
• PDF: https://arxiv.org/pdf/2511.16854
• Github: https://github.com/mkhateri/Awesome-MRI-Super-Resolution

🔹 Models citing this paper:
• https://huggingface.co/mkhateri/Awesome-MRI-Super-Resolution

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#DeepLearning #MRI #SuperResolution #MedicalImaging #AI