46 citations · 121 across the 23 of their papers we have counts for
33 papers · 1 filter
A Lagrangian View of Flow Matching
Peyman Milanfar
Modern explicit-time generative models, such as Flow Matching [Lipman et al., 2023] and Rectified Flow [Liu et al., 2023], are typically derived top-down via Optimal Transport and…
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…
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…