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RePaint: Inpainting using Denoising Diffusion Probabilistic Models
Andreas Lugmayr;Martin Danelljan;Andres Romero;Fisher Yu;Radu Timofte;Luc Van Gool
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (611)
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 of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of sem...Show More
NTIRE 2022 Challenge on Learning the Super-Resolution Space
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Kang-Wook Kim;Younggeun Kim;Jae-Young Lee;Zechao Li;Jinshan Pan;Dongseok Shim;Ki-Ung Song;Jinhui Tang;Cong Wang;Zhihao Zhao
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Cited by: Papers (20)
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the same low-resolution image, we asked the participants to create methods that sample diverse super-resolution from the space of possible high-resolution images given a low-resolution ima...Show More
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution
Andreas Lugmayr;Martin Danelljan;Fisher Yu;Luc Van Gool;Radu Timofte
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
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 is to employ pixel-wise losses, such as L1, which drive the prediction towards a blurry average. This leads to fundamentally conflicting objectives when combined with adversarial losses, which degrades the final quality...Show More
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Jingyun Liang;Andreas Lugmayr;Kai Zhang;Martin Danelljan;Luc Van Gool;Radu Timofte
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (57)
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-resolution (HR) images from the low-resolution (LR) image rather than learning a deterministic mapping. For image rescaling, it achieves high accuracy by jointly modelling the downscaling and upscaling processes. While existin...Show More
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
Valentin Wolf;Andreas Lugmayr;Martin Danelljan;Luc Van Gool;Radu Timofte
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (39)
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 synthesize realistic training data by modeling noise and degradations that appear in real-world settings. We propose DeFlow, a method for learning stochastic image degradations from unpaired data. Our approach is based on a ...Show More
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (24)
This paper reviews the NTIRE 2021 challenge on learning the super-Resolution space. It focuses on the participating methods and final results. The challenge addresses the problem of learning a model capable of predicting the space of plausible super-resolution (SR) images, from a single low-resolution image. The model must thus be capable of sampling diverse outputs, rather than just generating a ...Show More
NTIRE 2021 Learning the Super-Resolution Space Challenge
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Christoph Busch;Yang Chen;Jian Cheng;Vishal Chudasama;Ruipeng Gang;Shangqi Gao;Kun Gao;Laiyun Gong;Qingrui Han;Chao Huang;Zhi Jin;Younghyun Jo;Seon Joo Kim;Younggeun Kim;Seungjun Lee;Yuchen Lei;Chu-Tak Li;Chenghua Li;Ke Li;Zhi-Song Liu;Youming Liu;Nan Nan;Seung-Ho Park;Heena Patel;Shichong Peng;Kalpesh Prajapati;Haoran Qi;Kiran Raja;Raghavendra Ramachandra;Wan-Chi Siu;Donghee Son;Ruixia Song;Kishor Upla;Li-Wen Wang;Yatian Wang;Junwei Wang;Qianyu Wu;Xinhua Xu;Sejong Yang;Zhen Yuan;Liting Zhang;Huanrong Zhang;Junkai Zhang;Yifan Zhang;Zhenzhou Zhang;Hangqi Zhou;Aichun Zhu;Xiahai Zhuang;Jiaxin Zou
Year: 2021 | Conference Paper |
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Namhyuk Ahn;Dongwoon Bai;Jie Cai;Yun Cao;Junyang Chen;Kaihua Cheng;SeYoung Chun;Wei Deng;Mostafa El-Khamy;Chiu Man Ho;Xiaozhong Ji;Amin Kheradmand;Gwantae Kim;Hanseok Ko;Kanghyu Lee;Jungwon Lee;Hao Li;Ziluan Liu;Zhi-Song Liu;Shuai Liu;Yunhua Lu;Zibo Meng;Pablo Navarrete Michelini;Christian Micheloni;Kalpesh Prajapati;Haoyu Ren;Yong Hyeok Seo;Wan-Chi Siu;Kyung-Ah Sohn;Ying Tai;Rao Muhammad Umer;Shuangquan Wang;Huibing Wang;Timothy Haoning Wu;Haoning Wu;Biao Yang;Fuzhi Yang;Jaejun Yoo;Tongtong Zhao;Yuanbo Zhou;Haijie Zhuo;Ziyao Zong;Xueyi Zou
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2020 | Conference Paper |
Cited by: Papers (98)
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided along with a set of unpaired high-quality target images. In Track 1: Image Proc...Show More
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Namhyuk Ahn;Dongwoon Bai;Jie Cai;Yun Cao;Junyang Chen;Kaihua Cheng;SeYoung Chun;Wei Deng;Mostafa El-Khamy;Chiu Man Ho;Xiaozhong Ji;Amin Kheradmand;Gwantae Kim;Hanseok Ko;Kanghyu Lee;Jungwon Lee;Hao Li;Ziluan Liu;Zhi-Song Liu;Shuai Liu;Yunhua Lu;Zibo Meng;Pablo Navarrete Michelini;Christian Micheloni;Kalpesh Prajapati;Haoyu Ren;Yong Hyeok Seo;Wan-Chi Siu;Kyung-Ah Sohn;Ying Tai;Rao Muhammad Umer;Shuangquan Wang;Huibing Wang;Timothy Haoning Wu;Haoning Wu;Biao Yang;Fuzhi Yang;Jaejun Yoo;Tongtong Zhao;Yuanbo Zhou;Haijie Zhuo;Ziyao Zong;Xueyi Zou
Year: 2020 | Conference Paper |
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Manuel Fritsche;Shuhang Gu;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;Rajagoapalan A. N.;Nam Hyung Joon;Yu Seung Won;Guisik Kim;Dokyeong Kwon;Chih-Chung Hsu;Chia-Hsiang Lin;Yuanfei Huang;Xiaopeng Sun;Wen Lu;Jie Li;Xinbo Gao;Sefi Bell-Kligler;Assaf Shocher;Michal Irani
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (86)
This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such imag...Show More
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr;Martin Danelljan;Radu Timofte;Manuel Fritsche;Shuhang Gu;Kuldeep Purohit;Praveen Kandula;Maitreya Suin;Rajagoapalan A. N.;Nam Hyung Joon;Yu Seung Won;Guisik Kim;Dokyeong Kwon;Chih-Chung Hsu;Chia-Hsiang Lin;Yuanfei Huang;Xiaopeng Sun;Wen Lu;Jie Li;Xinbo Gao;Sefi Bell-Kligler;Assaf Shocher;Michal Irani
Year: 2019 | Conference Paper |
DIV8K: DIVerse 8K Resolution Image Dataset
Shuhang Gu;Andreas Lugmayr;Martin Danelljan;Manuel Fritsche;Julien Lamour;Radu Timofte
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (53)
Super-resolution methods in literature has in recent years been dominated by convolutional neural networks (CNNs), aiming to learn a direct mapping from a low to high resolution image. Although successful, these methods rely on large-scale and high-quality datasets, to learn more powerful models. The scale of the existing super-resolution datasets are limiting the performance of current deep and h...Show More
Unsupervised Learning for Real-World Super-Resolution
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (147)
Most current super-resolution methods rely on low and high resolution image pairs to train a network in a fully supervised manner. However, such image pairs are not available in real-world applications. Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image. Unfortunately, this strategy ...Show More
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