Search Results for author: Andreas Lugmayr

Found 9 papers, 6 papers with code

ReBotNet: Fast Real-time Video Enhancement

no code implementations23 Mar 2023 Jeya Maria Jose Valanarasu, Rahul Garg, Andeep Toor, Xin Tong, Weijuan Xi, Andreas Lugmayr, Vishal M. Patel, Anne Menini

The first branch learns spatio-temporal features by tokenizing the input frames along the spatial and temporal dimensions using a ConvNext-based encoder and processing these abstract tokens using a bottleneck mixer.

Video Enhancement Video Restoration

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

3 code implementations CVPR 2022 Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc van Gool

In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks.

Denoising Image Inpainting

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

no code implementations5 Nov 2021 Andreas Lugmayr, Martin Danelljan, Fisher Yu, Luc van Gool, Radu Timofte

Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions.


SRFlow: Learning the Super-Resolution Space with Normalizing Flow

6 code implementations ECCV 2020 Andreas Lugmayr, Martin Danelljan, Luc van Gool, Radu Timofte

SRFlow therefore directly accounts for the ill-posed nature of the problem, and learns to predict diverse photo-realistic high-resolution images.

Ranked #4 on Image Super-Resolution on DIV2K val - 4x upscaling (using extra training data)

Image Manipulation Image Super-Resolution

Unsupervised Learning for Real-World Super-Resolution

no code implementations20 Sep 2019 Andreas Lugmayr, Martin Danelljan, Radu Timofte

Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image.

Image Super-Resolution

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