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A generic framework which implements some famouts super-resolution models
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SR_Framework
A generic super-resolution framework which implements the following networks (Updating...)
DLSR [
Differentiable architecture search
]
RFDN [
AIM20 Champion
]
LatticeNet [
ECCV2020
]
IMDN [
ACM MM2019
]
SRFBN [
CVPR2019
]
IDN [
CVPR2018
]
CARN [
ECCV2018
]
RCAN [
ECCV2018
]
MemNet [
ICCV2017
]
EDSR [
CVPR2017
]
DRRN [
CVPR2017
]
LapSRN [
CVPR2017
]
DRCN [
CVPR2016
]
VDSR [
CVPR2016
]
FSRCNN [
ECCV 2016
]
Implement some useful functions for article figures. Like the following:
1. generate_best
: Automatically compare your method with other methods and visualize the best patches.
2. Frequency_analysis
: Convert an image to 1-D spectral densities.
3. relation
: Explore relations in fuse stage.(eg. torch.cat([t1, t2, t3, c4], dim=1) and then fuse them with 1x1 convolution)
4. feature_map
: Visualize feature map.(average feature maps along channel axis)
Open Source Agenda is not affiliated with "SR Framework" Project. README Source:
NJU-Jet/SR_Framework
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Open Issues
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Last Commit
2 years ago
Repository
NJU-Jet/SR_Framework
Tags
Carn
Drcn
Drrn
Edsr
Idn
Imdn
Lapsrn
Latticenet
Model Zoo
Pytorch
Srfbn
Super Resolution
Vdsr
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