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.
- ESPCN-PyTorch
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.
Contains DIV2K, DIV8K, Flickr2K, OST, T91, Set5, Set14, BSDS100 and BSDS200, etc.
Modify the contents of the file as follows.
- line 29:
upscale_factor
change to the magnification you need to enlarge. - line 31:
mode
change Set to valid mode. - line 71:
model_path
change weight address after training.
Modify the contents of the file as follows.
- line 29:
upscale_factor
change to the magnification you need to enlarge. - line 31:
mode
change Set to valid mode.
If you want to load weights that you've trained before, modify the contents of the file as follows.
- line 47:
start_epoch
change number of training iterations in the previous round. - line 48:
resume
change weight address that needs to be loaded.
Source of original paper results: https://arxiv.org/pdf/1609.05158v2.pdf
In the following table, the value in ()
indicates the result of the project, and -
indicates no test.
Dataset | Scale | PSNR |
---|---|---|
Set5 | 2 | -(36.59) |
Set5 | 3 | 32.55(32.50) |
Set5 | 4 | -(30.20) |
Low Resolution / Super Resolution / High Resolution
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}
}