PixelShuffle is an operation used in super-resolution models to implement efficient sub-pixel convolutions with a stride of $1/r$. Specifically it rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$.

Image Source: Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

Source: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

Latest Papers

PAPER DATE
Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset
Tong ZhengHirohisa ODAMasahiro OdaShota NAKAMURAMasaki MORIHirotsugu TAKABATAKEHiroshi NATORIKensaku MORI
2020-10-20
Attaining Real-Time Super-Resolution for Microscopic Images Using GAN
Vibhu BhatiaYatender Kumar
2020-10-09
Journey Towards Tiny Perceptual Super-Resolution
Royson LeeŁukasz DudziakMohamed AbdelfattahStylianos I. VenierisHyeji KimHongkai WenNicholas D. Lane
2020-07-08
Perceptual Extreme Super Resolution Network with Receptive Field Block
Taizhang ShangQiuju DaiShengchen ZhuTong YangYandong Guo
2020-05-26
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
| Yuzhi ZhaoLai-Man PoQiong YanWei LiuTingyu Lin
2020-05-10
Arbitrary Scale Super-Resolution for Brain MRI Images
Chuan TanJin ZhuPietro Lio'
2020-04-05
EndoL2H: Deep Super-Resolution for Capsule Endoscopy
| Yasin AlmaliogluKutsev Bengisu OzyorukAbdulkadir GokceKagan IncetanGuliz Irem GokcelerMuhammed Ali SimsekKivanc AraratRichard J. ChenNicholas J. DurrFaisal MahmoodMehmet Turan
2020-02-13
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging
Rewa SoodBinit TopiwalaKarthik ChoutaguntaRohit SoodMirabela Rusu
2019-12-19
Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks
Rewa SoodMirabela Rusu
2019-12-19
Image Super-Resolution Using a Wavelet-based Generative Adversarial Network
Qi ZhangHuafeng WangSichen Yang
2019-07-24
Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning
Ying Da WangRyan T. ArmstrongPeyman Mostaghimi
2019-07-15
SRGAN: Training Dataset Matters
Nao TakanoGita Alaghband
2019-03-24
SREdgeNet: Edge Enhanced Single Image Super Resolution using Dense Edge Detection Network and Feature Merge Network
Kwanyoung KimSe Young Chun
2018-12-18
Bi-GANs-ST for Perceptual Image Super-resolution
Xiaotong LuoRong ChenYuan XieYanyun QuCuihua Li
2018-11-01
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
Ke LiShichong PengJitendra Malik
2018-10-02
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
| Xintao WangKe YuShixiang WuJinjin GuYihao LiuChao DongChen Change LoyYu QiaoXiaoou Tang
2018-09-01
Wide Activation for Efficient and Accurate Image Super-Resolution
| Jiahui YuYuchen FanJianchao YangNing XuZhaowen WangXinchao WangThomas Huang
2018-08-27
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
| Xintao WangKe YuChao DongChen Change Loy
2018-04-09
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
| Wenzhe ShiJose CaballeroFerenc HuszárJohannes TotzAndrew P. AitkenRob BishopDaniel RueckertZehan Wang
2016-09-16

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