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20142024
most citedWeighted Schatten -Norm Minimization for Image Denoising and Background Subtraction

419 citations · 777 across the 33 of their papers we have counts for

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eess.IV2021

NTIRE 2021 Challenge on Perceptual Image Quality Assessment

Jinjin Gu, Haoming Cai, Chao Dong +47

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (…

eess.IV2021★ 9 cited

Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report

Andrey Ignatov, Andres Romero, Heewon Kim +28

Video super-resolution has recently become one of the most important mobile-related problems due to the rise of video communication and streaming services. While many solutions hav…

eess.IV2020★ 9 cited

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

Kai Zhang, Shuhang Gu, Radu Timofte +60

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 im…

eess.IV2019

Frequency Separation for Real-World Super-Resolution

Manuel Fritsche, Shuhang Gu, Radu Timofte

Most of the recent literature on image super-resolution (SR) assumes the availability of training data in the form of paired low resolution (LR) and high resolution (HR) images or…

eess.IV2019★ 19 cited

AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results

Kai Zhang, Shuhang Gu, Radu Timofte +26

This paper reviews the AIM 2019 challenge on constrained example-based single image super-resolution with focus on proposed solutions and results. The challenge had 3 tracks. Takin…

eess.IV2019

Efficient Video Super-Resolution through Recurrent Latent Space Propagation

Dario Fuoli, Shuhang Gu, Radu Timofte

With the recent trend for ultra high definition displays, the demand for high quality and efficient video super-resolution (VSR) has become more important than ever. Previous metho…