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AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results [article]

Dario Fuoli, Zhiwu Huang, Shuhang Gu, Radu Timofte, Arnau Raventos, Aryan Esfandiari, Salah Karout, Xuan Xu, Xin Li, Xin Xiong, Jinge Wang, Pablo Navarrete Michelini (+14 others)
2020 arXiv   pre-print
This paper reviews the video extreme super-resolution challenge associated with the AIM 2020 workshop at ECCV 2020.  ...  The task in this challenge is to upscale videos with an extreme factor of 16, which results in more serious degradations that also affect the structural integrity of the videos.  ...  Acknowledgements We thank the AIM 2020 sponsors: Huawei, MediaTek, NVIDIA, Qualcomm, Google, and Computer Vision Lab (CVL), ETH Zurich.  ... 
arXiv:2009.06290v1 fatcat:bbgfzmwupfgcnigwr2onun4zzm

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha (+51 others)
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.  ...  The challenge task was to super-resolve an input image with a magnification factor ×16 based on a set of prior examples of low and corresponding high resolution images.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: HUAWEI, OPPO, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
doi:10.1109/cvprw50498.2020.00254 dblp:conf/cvpr/ZhangGTSDZYGJYK20 fatcat:yvicfxmotjbhfk72f2noujonlq

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results [article]

Kai Zhang, Shuhang Gu, Radu Timofte, Taizhang Shang, Qiuju Dai, Shengchen Zhu, Tong Yang, Yandong Guo, Younghyun Jo, Sejong Yang, Seon Joo Kim, Lin Zha (+51 others)
2020 arXiv   pre-print
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.  ...  The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: HUAWEI, OPPO, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
arXiv:2005.01056v1 fatcat:6nwj5ilbgbgjnmd6oy435hjdhi

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results [article]

Kai Zhang, Martin Danelljan, Yawei Li, Radu Timofte, Jie Liu, Jie Tang, Gangshan Wu, Yu Zhu, Xiangyu He, Wenjie Xu, Chenghua Li, Cong Leng (+73 others)
2020 arXiv   pre-print
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results.  ...  The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.  ...  Acknowledgements We thank the AIM 2020 sponsors: HUAWEI, MediaTek, Google, NVIDIA, Qualcomm, and Computer Vision Lab (CVL) ETH Zurich. A Teams and affiliations  ... 
arXiv:2009.06943v1 fatcat:2s7k5wsgsjgo5flnqaby26cn64

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 (+34 others)
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results.  ...  This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: Huawei, Oppo, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
doi:10.1109/cvprw50498.2020.00255 dblp:conf/cvpr/LugmayrDTABCCCC20 fatcat:fmf4okszznffjfzat4ba3sksj4

NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results [article]

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 (+34 others)
2020 arXiv   pre-print
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on the participating methods and final results.  ...  This is the second challenge on the subject, following AIM 2019, targeting to advance the state-of-the-art in super-resolution. To measure the performance we use the benchmark protocol from AIM 2019.  ...  Acknowledgements We thank the NTIRE 2020 sponsors: Huawei, Oppo, Voyage81, MediaTek, DisneyResearch|Studios, and Computer Vision Lab (CVL) ETH Zurich.  ... 
arXiv:2005.01996v1 fatcat:ewngd7chdve3fbvwis32v64ruq

NTIRE 2019 Challenge on Video Deblurring: Methods and Results

Seungjun Nah, Radu Timofte, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Kyoung Mu Lee, Xintao Wang, Kelvin C.K. Chan, Ke Yu, Chao Dong, Chen Change Loy (+33 others)
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
This paper reviews the first NTIRE challenge on video deblurring (restoration of rich details and high frequency components from blurred video frames) with focus on the proposed solutions and results.  ...  A new REalistic and Diverse Scenes dataset (REDS) was employed. The challenge was divided into 2 tracks.  ...  Acknowledgments We thank the NTIRE 2019 sponsors: OPPO Mobile Corp., Ltd., NVIDIA Corp., HUAWEI Technologies Co. Ltd., SAMSUNG Electronics Co., Ltd., Amazon.com, Inc., MediaTek Inc., and ETH Zurich.  ... 
doi:10.1109/cvprw.2019.00249 dblp:conf/cvpr/NahTBHMSL19 fatcat:eieq2a6ms5bn7hx6cpccozhuyi

AIM 2020 Challenge on Video Temporal Super-Resolution [article]

Sanghyun Son, Jaerin Lee, Seungjun Nah, Radu Timofte, Kyoung Mu Lee
2020 arXiv   pre-print
This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis.  ...  The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.  ...  Acknowledgments We thank all AIM 2020 sponsors: Huawei Technologies Co. Ltd., MediaTek Inc., NVIDIA Corp., Qualcomm Inc., Google, LLC and CVL, ETH Zürich.  ... 
arXiv:2009.12987v1 fatcat:23cngd3i25bupb5lwszpzxthte

Video Super Resolution Based on Deep Learning: A Comprehensive Survey [article]

Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, Radu Timofte
2022 arXiv   pre-print
In this survey, we comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning.  ...  It is well known that the leverage of information within video frames is important for video super-resolution.  ...  Zekun Li (Master student at School of Artificial Intelligence in Xidian University) and Dr.  ... 
arXiv:2007.12928v3 fatcat:nxoejcfdnzas3jznbqsale36ty

Super-resolution Ultrasound Imaging

Kirsten Christensen-Jeffries, Olivier Couture, Paul A. Dayton, Yonina C. Eldar, Kullervo Hynynen, Fabian Kiessling, Meaghan O'Reilly, Gianmarco F. Pinton, Georg Schmitz, Meng-Xing Tang, Mickael Tanter, Ruud J.G. van Sloun
2020 Ultrasound in Medicine and Biology  
Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents.  ...  These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.  ...  Such displacements are significant relative the structures that super-resolution ultrasound aims to resolve.  ... 
doi:10.1016/j.ultrasmedbio.2019.11.013 pmid:31973952 fatcat:gt6l3av375e4dg7tebmcckcqdm

Learning to Have an Ear for Face Super-Resolution [article]

Givi Meishvili, Simon Jenni, Paolo Favaro
2020 arXiv   pre-print
We propose a novel method to use both audio and a low-resolution image to perform extreme face super-resolution (a 16x increase of the input size).  ...  Moreover, we show that our model builds a factorized representation of images and audio as it allows one to mix low-resolution images and audio from different videos and to generate realistic faces with  ...  Note that most methods in the literature are not trained on extreme super-resolution factors of 16×, but rather on factors of 4×.  ... 
arXiv:1909.12780v3 fatcat:u7lrnq6wovdqxm7txwervoyehq

Wide Activation for Efficient Image and Video Super-Resolution

Jiahui Yu, Yuchen Fan, Thomas S. Huang
2019 British Machine Vision Conference  
Based on WDSR, our method won 1st places in NTIRE 2018 Challenge on Single Image Super-Resolution in all three realistic tracks.  ...  Moreover, a simple frameconcatenation based WDSR achieved 2nd places in three out of four tracks of NTIRE 2019 Challenge for Video Super-Resolution and Deblurring.  ...  in NTIRE 2019 Challenges tested on 4x bicubic down-sampling for super-resolution task.  ... 
dblp:conf/bmvc/YuFH19 fatcat:qh7fyegi3jg5valcjjermywqty

Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling [article]

Hongying Liu, Peng Zhao, Zhubo Ruan, Fanhua Shang, Yuanyuan Liu
2021 arXiv   pre-print
Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR).  ...  Our experimental results confirm that our method achieves superior performance on videos with large motion compared to state-of-the-art methods.  ...  Acknowledgments This work was supported by the National Natural Science Foundation of China (Nos. 61976164, 61876220, 61876221, 61836009, U1701267, and 61871310)  ... 
arXiv:2103.11744v1 fatcat:33rwxdijybgffamponryrx2lqm

A Deep Journey into Super-resolution: A survey [article]

Saeed Anwar, Salman Khan, Nick Barnes
2020 arXiv   pre-print
In this exposition, we extensively compare 30+ state-of-the-art super-resolution Convolutional Neural Networks (CNNs) over three classical and three recently introduced challenging datasets to benchmark  ...  single image super-resolution.  ...  The super-resolution methods [47] , [81] based on the GAN framework are explained next. SRGAN Single image super-resolution by large up-scaling factors is very challenging.  ... 
arXiv:1904.07523v3 fatcat:ovihxjadfja55hrytvhggj5c6q

Image Super-Resolution by Neural Texture Transfer [article]

Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi
2019 arXiv   pre-print
Due to the significant information loss in low-resolution (LR) images, it has become extremely challenging to further advance the state-of-the-art of single image super-resolution (SISR).  ...  Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input  ...  Reference-based Super-Resolution In contrast to SISR where only a single LR image is used as input, RefSR methods introduce additional images to assist the SR process.  ... 
arXiv:1903.00834v2 fatcat:z7ccwskyzvcrnf6xc2vvc3vvni
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