76 citations · 100 across the 7 of their papers we have counts for
9 papers
ReBotNet: Fast Real-time Video Enhancement
Jeya Maria Jose Valanarasu, Rahul Garg, Andeep Toor +5
Most video restoration networks are slow, have high computational load, and can't be used for real-time video enhancement. In this work, we design an efficient and fast framework t…
RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Andreas Lugmayr, Martin Danelljan, Andres Romero +3
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution o…
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution
Andreas Lugmayr, Martin Danelljan, Fisher Yu +2
Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions. Yet, the dominant paradigm…
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Jingyun Liang, Andreas Lugmayr, Kai Zhang +3
Normalizing flows have recently demonstrated promising results for low-level vision tasks. For image super-resolution (SR), it learns to predict diverse photo-realistic high-resolu…
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
Valentin Wolf, Andreas Lugmayr, Martin Danelljan +2
The difficulty of obtaining paired data remains a major bottleneck for learning image restoration and enhancement models for real-world applications. Current strategies aim to synt…
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Andreas Lugmayr, Martin Danelljan, Luc Van Gool +1
Super-resolution is an ill-posed problem, since it allows for multiple predictions for a given low-resolution image. This fundamental fact is largely ignored by state-of-the-art de…