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‎README.md‎

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# ESPCN-PyTorch
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# Fork of [Lornatang/ESPCN-PyTorch](https://github.com/Lornatang/ESPCN-PyTorch)
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## Overview
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Differences between original repository and fork:
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This repository contains an op-for-op PyTorch reimplementation of [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://arxiv.org/abs/1609.05158v2).
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* Compatibility with PyTorch >=2.0. (🔥)
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* Installation with updated [requirements.txt](requirements.txt) file.
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## Table of contents
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# Installation
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- [ESPCN-PyTorch](#espcn-pytorch)
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- [Overview](#overview)
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- [Table of contents](#table-of-contents)
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- [About Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](#about-real-time-single-image-and-video-super-resolution-using-an-efficient-sub-pixel-convolutional-neural-network)
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- [Download weights](#download-weights)
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- [Download datasets](#download-datasets)
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- [How Test and Train](#how-test-and-train)
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- [Test ESPCN_x4](#test-espcn_x4)
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- [Train ESPCN_x4](#train-espcn_x4)
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- [Resume ESPCN_x4](#resume-train-espcn_x4)
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- [Result](#result)
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- [Credit](#credit)
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- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](#real-time-single-image-and-video-super-resolution-using-an-efficient-sub-pixel-convolutional-neural-network)
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## About Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
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If you're new to ESPCN, here's an abstract straight from the paper:
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Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational
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performance for single image super-resolution. In these methods, the low resolution (LR)
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input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means
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that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this
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paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose
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a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer
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which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the
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handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the
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computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and
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show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based
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methods.
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## Download weights
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- [Google Driver](https://drive.google.com/drive/folders/17ju2HN7Y6pyPK2CC_AqnAfTOe9_3hCQ8?usp=sharing)
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- [Baidu Driver](https://pan.baidu.com/s/1yNs4rqIb004-NKEdKBJtYg?pwd=llot)
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## Download datasets
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Contains DIV2K, DIV8K, Flickr2K, OST, T91, Set5, Set14, BSDS100 and BSDS200, etc.
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- [Google Driver](https://drive.google.com/drive/folders/1A6lzGeQrFMxPqJehK9s37ce-tPDj20mD?usp=sharing)
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- [Baidu Driver](https://pan.baidu.com/s/1o-8Ty_7q6DiS3ykLU09IVg?pwd=llot)
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Please refer to `README.md` in the `data` directory for the method of making a dataset.
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## How Test and Train
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Both training and testing only need to modify the `config.py` file.
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### Test ESPCN_x4
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Modify the `config.py` file.
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- line 31: `model_arch_name` change to `espcn_x4`.
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- line 36: `upscale_factor` change to `4`.
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- line 38: `mode` change to `test`.
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- line 40: `exp_name` change to `ESPCN_x4-Set5`.
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- line 84: `lr_dir` change to `f"./data/Set5/LRbicx{upscale_factor}"`.
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- line 86: `gt_dir` change to `f"./data/Set5/GTmod12"`.
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- line 88: `model_weights_path` change to `./results/pretrained_models/ESPCN_x4-T91-64bf5ee4.pth.tar`.
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```bash
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python3 test.py
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```
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### Train ESPCN_x4
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Modify the `config.py` file.
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- line 31: `model_arch_name` change to `espcn_x4`.
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- line 36: `upscale_factor` change to `4`.
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- line 38: `mode` change to `test`.
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- line 40: `exp_name` change to `ESPCN_x4-Set5`.
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- line 84: `lr_dir` change to `f"./data/Set5/LRbicx{upscale_factor}"`.
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- line 86: `gt_dir` change to `f"./data/Set5/GTmod12"`.
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```bash
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python3 train.py
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```shell
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pip install -r requirements.txt
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```
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### Resume train ESPCN_x4
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# Pretrained models
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Modify the `config.py` file.
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| Name | Scale | Set5 (PSNR) | Set14 (PSNR) | Link |
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|----------|-------|-------------|--------------|------------------------------------------------------------------------------------------------------------|
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| ESPCN_x2 | 2 | 36.64 | 32.35 | [PyTorch](https://github.com/clibdev/ESPCN-PyTorch/releases/latest/download/ESPCN_x2-T91-da809cd7.pth.tar) |
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| ESPCN_x3 | 3 | 32.55 | 29.20 | [PyTorch](https://github.com/clibdev/ESPCN-PyTorch/releases/latest/download/ESPCN_x3-T91-647e91f3.pth.tar) |
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| ESPCN_x4 | 4 | 30.26 | 27.41 | [PyTorch](https://github.com/clibdev/ESPCN-PyTorch/releases/latest/download/ESPCN_x4-T91-64bf5ee4.pth.tar) |
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- line 31: `model_arch_name` change to `espcn_x4`.
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- line 36: `upscale_factor` change to `4`.
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- line 38: `mode` change to `test`.
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- line 40: `exp_name` change to `ESPCN_x4-Set5`.
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- line 57: `resume_model_weights_path` change to `./samples/ESPCN_x4-Set5/epoch_xxx.pth.tar`.
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- line 84: `lr_dir` change to `f"./data/Set5/LRbicx{upscale_factor}"`.
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- line 86: `gt_dir` change to `f"./data/Set5/GTmod12"`.
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# Inference
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```bash
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python3 train.py
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```
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## Result
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Source of original paper results: [https://arxiv.org/pdf/1609.05158v2.pdf](https://arxiv.org/pdf/1609.05158v2.pdf)
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In the following table, the value in `()` indicates the result of the project, and `-` indicates no test.
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| Method | Scale | Set5 (PSNR) | Set14 (PSNR) |
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|:--------:|:-----:|:----------------:|:----------------:|
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| ESPCN_x4 | 2 | -(**36.64**) | -(**32.35**) |
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| ESPCN_x3 | 3 | 32.55(**32.55**) | 29.08(**29.20**) |
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| ESPCN_x4 | 4 | 30.90(**30.26**) | 27.73(**27.41**) |
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```bash
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# Download `ESPCN_x4-T91-64bf5ee4.pth.tar` weights to `./results/pretrained_models/ESPCN_x4-T91-64bf5ee4.pth.tar`
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# More detail see `README.md<Download weights>`
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python3 ./inference.py
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```
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Input:
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<span align="center"><img width="240" height="360" src="figure/comic.png"/></span>
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Output:
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<span align="center"><img width="240" height="360" src="figure/sr_comic.png"/></span>
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```text
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Build `espcn_x4` model successfully.
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Load `espcn_x4` model weights `./results/pretrained_models/ESPCN_x4-T91-64bf5ee4.pth.tar` successfully.
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SR image save to `./figure/sr_comic.png`
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```
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### Credit
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#### Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
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_Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang_ <br>
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**Abstract** <br>
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Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational
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performance for single image super-resolution. In these methods, the low resolution (LR)
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input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means
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that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this
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paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose
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a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer
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which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the
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handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the
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computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and
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show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based
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methods.
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[[Paper]](https://arxiv.org/pdf/1609.05158)
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```
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@article{DBLP:journals/corr/ShiCHTABRW16,
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author = {Wenzhe Shi and
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Jose Caballero and
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Ferenc Husz{\'{a}}r and
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Johannes Totz and
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Andrew P. Aitken and
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Rob Bishop and
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Daniel Rueckert and
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Zehan Wang},
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title = {Real-Time Single Image and Video Super-Resolution Using an Efficient
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Sub-Pixel Convolutional Neural Network},
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journal = {CoRR},
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volume = {abs/1609.05158},
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year = {2016},
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url = {http://arxiv.org/abs/1609.05158},
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archivePrefix = {arXiv},
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eprint = {1609.05158},
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timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
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biburl = {https://dblp.org/rec/journals/corr/ShiCHTABRW16.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```shell
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python inference.py --model_arch_name espcn_x2 --upscale_factor 2 --model_weights_path results/pretrained_models/ESPCN_x2-T91-da809cd7.pth.tar --inputs_path figure/comic.png --output_path figure/sr_comic_x2.png
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python inference.py --model_arch_name espcn_x3 --upscale_factor 3 --model_weights_path results/pretrained_models/ESPCN_x3-T91-647e91f3.pth.tar --inputs_path figure/comic.png --output_path figure/sr_comic_x3.png
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python inference.py --model_arch_name espcn_x4 --upscale_factor 4 --model_weights_path results/pretrained_models/ESPCN_x4-T91-64bf5ee4.pth.tar --inputs_path figure/comic.png --output_path figure/sr_comic_x4.png
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```

‎inference.py‎

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import cv2
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import torch
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from flatbuffers.builder import np
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import numpy as np
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from torch import nn
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import config

‎requirements.txt‎

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opencv-python
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numpy
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tqdm
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torch
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setuptools
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torchvision
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natsort
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torch>=2.0.0
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torchvision>=0.15.0
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opencv-python>=4.7.0
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tqdm>=4.65.0
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scipy>=1.10.0
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natsort>=8.4.0

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