IDaS-SR performs one-step real-world super-resolution by predicting severity-aware timesteps to anchor low-quality latents and using continuous trajectory steering to balance structure and texture generation.
arXiv preprint arXiv:1803.07422 (2018) 10
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CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
CoD-Lite delivers real-time generative image compression via a lightweight convolution-based diffusion codec with compression-oriented pre-training and distillation, achieving substantial bitrate savings.
citing papers explorer
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Bridging Restoration and Generation Manifolds in One-Step Diffusion for Real-World Super-Resolution
IDaS-SR performs one-step real-world super-resolution by predicting severity-aware timesteps to anchor low-quality latents and using continuous trajectory steering to balance structure and texture generation.
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CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder
CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
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CoD-Lite: Real-Time Diffusion-Based Generative Image Compression
CoD-Lite delivers real-time generative image compression via a lightweight convolution-based diffusion codec with compression-oriented pre-training and distillation, achieving substantial bitrate savings.