activity
20202024
most citedActivating More Pixels in Image Super-Resolution Transformer

55 citations · 162 across the 24 of their papers we have counts for

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6 papers · 1 filter

eess.IV2022★ 2 cited

MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

Ruicheng Feng, Chongyi Li, Shangchen Zhou +6

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobi…

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★ 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…

eess.IV2021

A New Journey from SDRTV to HDRTV

Xiangyu Chen, Zhengwen Zhang, Jimmy S. Ren +3

Nowadays modern displays are capable to render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most available resources are still in standard dynam…

eess.IV2021★ 6 cited

HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization

Xiangyu Chen, Yihao Liu, Zhengwen Zhang +2

Most consumer-grade digital cameras can only capture a limited range of luminance in real-world scenes due to sensor constraints. Besides, noise and quantization errors are often i…