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[GSoC '19] Learning-based Super-Resolution in OpenCV

Google Summer of Code 2019 with OpenCV

Learning-based Super Resolution

Student: Xavier Weber
Mentors: Vladimir Tyan & Yida Wang
Student on the same project: Fanny Monori

Link to accomplished work:

Intro

Hello, I am Xavier! I was a student developer for GSoC 2019 with OpenCV (Link to Project).

The main goal of this project was to add a new module to OpenCV: dnn_superres. This module allows for upscaling images via Convolutional Neural Networks. Easy access to popular Super Resolution data was also included.

The module delivers a simple-to-use interface that effectively uses the state-of-the-art super resolution techniques. This enables developers (that have little or no knowledge about deep learning or super resolution) to easily use this tool in his/her project. A developer can input an image or even real-time video, select their desired method and up-scaling factor and get as output their imagery with up-scaled resolution.

During this project I worked closely with Fanny. She worked on the same module but implemented different models: ESPCN [2] and LapSRN [4]. I implemented EDSR [1] and FSRCNN [3]. These are all supported in this module.

My Journey

First Period

In the first month of GSoC I implemented the following:

  • Loading code for three popular SR datasets: Div2k, BSDS and General100.
    This allows for easy access to high-quality images inside of the OpenCV environment.

  • Creating the 'dnn_superres' module which is the interface that connects users to all the trained models.
    This simplifies the use of these trained models by handling all the pre- and post-processing.

  • FSRCNN [3]
    This is a pretty light-weight network that can upscale images fast and has decent performance.

Second Period

In the second month of GSoC I implemented the following:

  • EDSR [1]
    This is a state-of-the-art high performance neural network, albeit quite slow.

  • Updated the 'dnn_superres' module with pre- and post-processing to support this new network.

Third Period

In the last month of GSoC I implemented the following:

  • Finished the models and trained them for scales x2, x3 and x4
  • Coordinating with Fanny to complete the module
  • Documentation
  • Test
  • Tutorial

Demonstration

Here I will demonstrate how you can use this module and reveal its POWER!

1. Build

Make sure you build OpenCV with the contrib-modules, including "dnn_superres".

2. Upscale

Now we can upscale any images using state-of-the-art techniques, by only using OpenCV!!

#include <opencv2/dnn_superres.hpp>
using namespace std;
using namespace cv;
using namespace dnn;
using namespace dnn_superres;

//Create the object
DnnSuperResImpl sr;

//Read the desired model - download links below
path = "models/EDSR_x4.pb"
sr.readModel(path);

//Set the desired model and scale to get correct pre- and post-processing
sr.setModel("edsr", 4);

//Upscale
Mat img = cv::imread(img_path);
Mat img_new;
sr.upsample(img, img_new);

You can download my models here: EDSR and FSRCNN, and Fanny's models here: ESPCN and LapSRN.

3. Results

Input:

input

Bicubic upscale:

bicubic

FSRCNN upscale:

fsrcnn

EDSR upscale:

edsr

Original:

monarch

Benchmarks on the General100 dataset

All computations were done on an i7-9700K. Metrics used are PSNR and SSIM.

2x scaling factor
Avg inference time in sec (CPU) Avg PSNR Avg SSIM
ESPCN 0.008795 32.7059 0.9276
EDSR 5.923450 34.1300 0.9447
FSRCNN 0.021741 32.8886 0.9301
LapSRN 0.114812 32.2681 0.9248
Bicubic 0.000208 32.1638 0.9305
Nearest neighbor 0.000114 29.1665 0.9049
Lanczos 0.001094 32.4687 0.9327
4x scaling factor
Avg inference time in sec (CPU) Avg PSNR Avg SSIM
ESPCN 0.004311 26.6870 0.7891
EDSR 1.607570 28.1552 0.8317
FSRCNN 0.005302 26.6088 0.7863
LapSRN 0.121229 26.7383 0.7896
Bicubic 0.000311 26.0635 0.8754
Nearest neighbor 0.000148 23.5628 0.8174
Lanczos 0.001012 25.9115 0.8706

References

[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution", 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. [PDF] [arXiv] [Slide]

[2] Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D. and Wang, Z., "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network", Proceedings of the IEEE conference on computer vision and pattern recognition CVPR 2016. [PDF] [arXiv]

[3] Chao Dong, Chen Change Loy, Xiaoou Tang. "Accelerating the Super-Resolution Convolutional Neural Network", in Proceedings of European Conference on Computer Vision ECCV 2016. [PDF] [arXiv] [Project Page]

[4] Lai, W. S., Huang, J. B., Ahuja, N., and Yang, M. H., "Deep laplacian pyramid networks for fast and accurate super-resolution", In Proceedings of the IEEE conference on computer vision and pattern recognition CVPR 2017. [PDF] [arXiv] [Project Page]

@StevenPuttemans

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@StevenPuttemans StevenPuttemans commented Aug 22, 2019

Nice write-up and great contribution 🎉 !

@moeabdol

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@moeabdol moeabdol commented Jul 2, 2020

Great work and explanation 👍

@jaivanti

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@jaivanti jaivanti commented Nov 15, 2021

So, does with increasing upscale factor for ESPCN model, the inference time decreases?

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