419 citations · 777 across the 33 of their papers we have counts for
7 papers · 1 filter
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +18
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,…
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…
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…
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…
Learning Filter Basis for Convolutional Neural Network Compression
Yawei Li, Shuhang Gu, Luc Van Gool +1
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of im…
Exemplar Guided Face Image Super-Resolution without Facial Landmarks
Berk Dogan, Shuhang Gu, Radu Timofte
Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images. Therefore, for super-resolving a given very low-resolutio…
Learning Content-Weighted Deep Image Compression
Mu Li, Wangmeng Zuo, Shuhang Gu +2
Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance. Most existing methods adopt spatially invariant bit length allocation…