13 papers
Generative Manifold Distillation: Aligning Restoration Trajectories with Natural Image Prior
Yuyang Hu, Mojtaba Sahraee-Ardakan, Arpit Bansal +4
Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domains is challenging as real-world data lacks paired groun…
The Geometry of Noise: Why Diffusion Models Don't Need Noise Conditioning
Mojtaba Sahraee-Ardakan, Mauricio Delbracio, Peyman Milanfar
Autonomous (noise-agnostic) generative models, such as Equilibrium Matching and blind diffusion, challenge the standard paradigm by learning a single, time-invariant vector field t…
Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
Yuyang Hu, Kangfu Mei, Mojtaba Sahraee-Ardakan +3
Diffusion models show promise for image restoration, but existing methods often struggle with inconsistent fidelity and undesirable artifacts. To address this, we introduce Kernel…
UniRes: Universal Image Restoration for Complex Degradations
Mo Zhou, Keren Ye, Mauricio Delbracio +3
Real-world image restoration is hampered by diverse degradations stemming from varying capture conditions, capture devices and post-processing pipelines. Existing works make improv…
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
Keren Ye, Ignacio Garcia Dorado, Michalis Raptis +4
While recent advancements in Image Super-Resolution (SR) using diffusion models have shown promise in improving overall image quality, their application to scene text images has re…
Reference-Guided Identity Preserving Face Restoration
Mo Zhou, Keren Ye, Viraj Shah +5
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods o…