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GaintPandaSR

A Image Super-Resolution Codebase which implements all sota approaches.

1. Dataset

Public Datasets:
   |- DIV2K
   |- Flicker2K
   |- DIV8K
   |- Set5

2. Code Role

We use the PEP8 style and we should add docstring to every new function.

3. Folder structure

. ├── data │   └── init.py ├── log │   └── init.py ├── metrics │   └── init.py ├── model │   └── init.py ├── README.md ├── requirements.txt ├── tools │   ├── demo.py │   ├── test.py │   └── train.py └── utils └── init.py

4. Papers & Codes

Name Summary Paper Code
2015
SRCNN Image Super-Resolution Using Deep Convolutional Networks [arXiv] [code]
2016
SRGAN Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [arXiv] [code]
FSRGAN Accelerating the Super-Resolution Convolutional Neural Network [arXiv] [code]
EnhanceNet Single Image Super-Resolution Through Automated Texture Synthesis [arXiv] [code]
2017
LapSRN Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution [arXiv] [code]
EDSR Enhanced Deep Residual Networks for Single Image Super-Resolution [arXiv] [code]
2018