Search Results for author: Andreas Lugmayr

Found 8 papers, 6 papers with code

RePaint: Inpainting using Denoising Diffusion Probabilistic Models

1 code implementation 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

4 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.

Image Manipulation 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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