318 citations · 1.1k across the 52 of their papers we have counts for
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A Systematic Survey of Deep Learning-based Single-Image Super-Resolution
Juncheng Li, Zehua Pei, Wenjie Li +4
Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has…
Light Field Image Super-Resolution with Transformers
Zhengyu Liang, Yingqian Wang, Longguang Wang +2
Light field (LF) image super-resolution (SR) aims at reconstructing high-resolution LF images from their low-resolution counterparts. Although CNN-based methods have achieved remar…
Dense Nested Attention Network for Infrared Small Target Detection
Boyang Li, Chao Xiao, Longguang Wang +5
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded p…
Unsupervised Degradation Representation Learning for Blind Super-Resolution
Longguang Wang, Yingqian Wang, Xiaoyu Dong +4
Most existing CNN-based super-resolution (SR) methods are developed based on an assumption that the degradation is fixed and known (e.g., bicubic downsampling). However, these meth…