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20152023
most citedReal-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

85 citations · 713 across the 54 of their papers we have counts for

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Showing 2022 · eess.IVShow all

5 papers · 2 filters

eess.IV2022★ 8 cited

Efficient Image Super-Resolution using Vast-Receptive-Field Attention

Lin Zhou, Haoming Cai, Jinjin Gu +5

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechan…

eess.IV2022★ 2 cited

UDC-UNet: Under-Display Camera Image Restoration via U-Shape Dynamic Network

Xina Liu, Jinfan Hu, Xiangyu Chen +1

Under-Display Camera (UDC) has been widely exploited to help smartphones realize full screen display. However, as the screen could inevitably affect the light propagation process,…

eess.IV2022

A Closer Look at Blind Super-Resolution: Degradation Models, Baselines, and Performance Upper Bounds

Wenlong Zhang, Guangyuan Shi, Yihao Liu +2

Degradation models play an important role in Blind super-resolution (SR). The classical degradation model, which mainly involves blur degradation, is too simple to simulate real-wo…

eess.IV2022★ 1 cited

VFHQ: A High-Quality Dataset and Benchmark for Video Face Super-Resolution

Liangbin Xie. Xintao Wang, Honglun Zhang, Chao Dong +1

Most of the existing video face super-resolution (VFSR) methods are trained and evaluated on VoxCeleb1, which is designed specifically for speaker identification and the frames in…

eess.IV2022★ 55 cited

Activating More Pixels in Image Super-Resolution Transformer

Xiangyu Chen, Xintao Wang, Jiantao Zhou +2

Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limit…