Damonfruit / SRCNN-PyTorch

Pytorch framework can easily implement srcnn algorithm with excellent performance

SRCNN-PyTorch

Overview

This repository contains an op-for-op PyTorch reimplementation of Image Super-Resolution Using Deep Convolutional Networks.

Table of contents

• SRCNN-PyTorch
  • Overview
  • Table of contents
  • About Image Super-Resolution Using Deep Convolutional Networks
  • Download weights
  • Download datasets
    • Download train dataset
    • Download valid dataset
  • Test
  • Train
  • Result
  • Credit
    • Image Super-Resolution Using Deep Convolutional Networks

About Image Super-Resolution Using Deep Convolutional Networks

If you're new to SRCNN, here's an abstract straight from the paper:

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

Download weights

• Google Driver
• Baidu Driver access:llot

Download datasets

Download train dataset

T91

• Image format

  • Google Driver
  • Baidu Driver access: llot

• LMDB format (train)

  • Google Driver
  • Baidu Driver access: llot

• LMDB format (valid)

  • Google Driver
  • Baidu Driver access: llot

Download valid dataset

Set5

• Image format
  • Google Driver
  • Baidu Driver access:llot

Set14

• Image format
  • Google Driver
  • Baidu Driver access:llot

BSD100

• Image format
  • Google Driver
  • Baidu Driver access:llot

Test

Modify the contents of the file as follows.

• line 24: upscale_factor change to the magnification you need to enlarge.
• line 25: mode change Set to valid mode.
• line 77: model_path change weight address after training.

Train

Modify the contents of the file as follows.

• line 24: upscale_factor change to the magnification you need to enlarge.
• line 25: mode change Set to train mode.

If you want to load weights that you've trained before, modify the contents of the file as follows.

• line 47: resume change to True.
• line 48: strict Transfer learning is set to False, incremental learning is set to True.
• line 49: start_epoch change number of training iterations in the previous round.
• line 50: resume_weight the weight address that needs to be loaded.

Result

Source of original paper results: https://arxiv.org/pdf/1501.00092v3.pdf

In the following table, the value in () indicates the result of the project, and - indicates no test.

Dataset	Scale	PSNR
Set5	2	36.34(36.31)
Set5	3	32.39(32.31)
Set5	4	30.09(29.95)

Low Resolution / Super Resolution / High Resolution

Credit

Image Super-Resolution Using Deep Convolutional Networks

Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang

Abstract
We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage. We explore different network structures and parameter settings to achieve trade-offs between performance and speed. Moreover, we extend our network to cope with three color channels simultaneously, and show better overall reconstruction quality.

[Paper] [Author's implements(Caffe)]

@misc{dong2014image,
    title={Image Super-Resolution Using Deep Convolutional Networks},
    author={Chao Dong and Chen Change Loy and Kaiming He and Xiaoou Tang},
    year={2014},
    eprint={1501.00092},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}