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.
- Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
- Installation
- Test
- Train
- Contributing
- Credit
About Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network.
If you're new to ESPCN, here's an abstract straight from the paper:
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this 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 a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer 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 handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and 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 methods.
git clone https://github.com/Lornatang/ESPCN-PyTorch.git
cd ESPCN-PyTorch/
pip install -r requirements.txtcd weights/
bash download_weights.shcd data/
bash download_dataset.shEvaluate the overall performance of the network.
usage: test.py [-h] [--dataroot DATAROOT] [--scale-factor {2,3,4,8}]
[--weights WEIGHTS] [--cuda]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel
Convolutional Neural Network..
optional arguments:
-h, --help show this help message and exit
--dataroot DATAROOT The directory address where the image needs to be
processed. (default: `./data/Set5`).
--scale-factor {2,3,4,8}
Image scaling ratio. (default: 4).
--weights WEIGHTS Generator model name. (default:`weights/espcn_4x.pth`)
--cuda Enables cuda
# Example
python test.py --dataroot ./data/Set5 --scale-factor 4 --weights ./weights/espcn_4x.pth --cudaEvaluate the benchmark of validation data set in the network
usage: test_benchmark.py [-h] [--dataroot DATAROOT] [-j N]
[--image-size IMAGE_SIZE] --scale-factor {2,3,4,8}
--weights WEIGHTS [--cuda]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel
Convolutional Neural Network.
optional arguments:
-h, --help show this help message and exit
--dataroot DATAROOT Path to datasets. (default:`./data/VOC2012`)
-j N, --workers N Number of data loading workers. (default:4)
--image-size IMAGE_SIZE
Size of the data crop (squared assumed). (default:256)
--scale-factor {2,3,4,8}
Low to high resolution scaling factor.
--weights WEIGHTS Path to weights.
--cuda Enables cuda
# Example
python test_benchmark.py --dataroot ./data/VOC2012 --scale-factor 4 --weights ./weights/espcn_4x.pth --cudaTest single picture
usage: test_image.py [-h] [--file FILE] [--scale-factor {2,3,4,8}]
[--weights WEIGHTS] [--cuda]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel
Convolutional Neural Network.
optional arguments:
-h, --help show this help message and exit
--file FILE Test low resolution image name.
(default:`./assets/baby.png`)
--scale-factor {2,3,4,8}
Super resolution upscale factor. (default:4)
--weights WEIGHTS Generator model name. (default:`weights/espcn_4x.pth`)
--cuda Enables cuda
# Example
python test_image.py --file ./assets/baby.png --scale-factor 4 ---weights ./weights/espcn_4x.pth -cudaTest single video
usage: test_video.py [-h] --file FILE --weights WEIGHTS --scale-factor
{2,3,4,8} [--view] [--cuda]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel
Convolutional Neural Network.
optional arguments:
-h, --help show this help message and exit
--file FILE Test low resolution video name.
--weights WEIGHTS Generator model name.
--scale-factor {2,3,4,8}
Super resolution upscale factor. (default:4)
--view Super resolution real time to show.
--cuda Enables cuda
# Example
python test_video.py --file ./data/1.mp4 --scale-factor 4 --weights ./weights/espcn_4x.pth --view --cudaLow resolution / Recovered High Resolution / Ground Truth
usage: train.py [-h] [--dataroot DATAROOT] [-j N] [--epochs N]
[--image-size IMAGE_SIZE] [-b N] [--lr LR]
[--scale-factor {2,3,4,8}] [--weights WEIGHTS] [-p N]
[--manualSeed MANUALSEED] [--cuda]
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel
Convolutional Neural Network.
optional arguments:
-h, --help show this help message and exit
--dataroot DATAROOT Path to datasets. (default:`./data/VOC2012`)
-j N, --workers N Number of data loading workers. (default:4)
--epochs N Number of total epochs to run. (default:100)
--image-size IMAGE_SIZE
Size of the data crop (squared assumed). (default:256)
-b N, --batch-size N mini-batch size (default: 64), this is the total batch
size of all GPUs on the current node when using Data
Parallel or Distributed Data Parallel.
--lr LR Learning rate. (default:0.01)
--scale-factor {2,3,4,8}
Low to high resolution scaling factor. (default:4).
--weights WEIGHTS Path to weights (to continue training).
-p N, --print-freq N Print frequency. (default:5)
--manualSeed MANUALSEED
Seed for initializing training. (default:0)
--cuda Enables cudapython train.py --dataroot ./data/VOC2012 --scale-factor 4 --cudaIf you want to load weights that you've trained before, run the following command.
python train.py --dataroot ./data/VOC2012 --scale-factor 4 --weights ./weights/espcn_4x_epoch_100.pth --cudaIf you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
I look forward to seeing what the community does with these models!
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang
Abstract
Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction
accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR)
input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation,
before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate
that this is sub-optimal and adds computational complexity. In this 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 a novel CNN
architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient
sub-pixel convolution layer 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 handcrafted bicubic filter in the SR pipeline with more
complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity
of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets
and 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 methods.
@article{DBLP:journals/corr/ShiCHTABRW16,
author = {Wenzhe Shi and
Jose Caballero and
Ferenc Husz{\'{a}}r and
Johannes Totz and
Andrew P. Aitken and
Rob Bishop and
Daniel Rueckert and
Zehan Wang},
title = {Real-Time Single Image and Video Super-Resolution Using an Efficient
Sub-Pixel Convolutional Neural Network},
journal = {CoRR},
volume = {abs/1609.05158},
year = {2016},
url = {http://arxiv.org/abs/1609.05158},
archivePrefix = {arXiv},
eprint = {1609.05158},
timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/ShiCHTABRW16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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