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SRGAN-VGG54 Single Image Super Resolution Matlab port.

version 1.0.0 (5.04 MB) by manoreken
SRGAN-VGG54 Single Image Super Resolution Matlab port. Inputs pristine image and performs 2x upsampling using a deep learning.

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Updated 03 Jul 2021

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SRGAN-VGG54 Single Image Super Resolution Matlab port.
■ Prerequisites ■
  • Matlab 2021a
  • Image Processing toolbox
  • Statistics and Machine Learning toolbox
  • Deep Learning Toolbox
  • Parallel Computing Toolbox
■ How to Test ■
Run SRGAN_Test.m which calls SRGAN_2xSuperResolution.m
Trained net is loaded on the line 5 of SRGAN_2xSuperResolution.m
■ How to Perform SRGAN Super-Resolution to your image file ■
Input image MyPicture.jpg should be pristine (not blurred) image. SRGAN neural net will upscale the image by 2x.
img = imread("MyPicture.jpg"); % 1024x768 input image
imgSR = SRGAN_2xSuperResolution(img);
imwrite(imgSR, "MyPicture_2x_SRGAN_MSE.png"); % 2048x1536 image is outputted
■ How to Train the network using Flickr2K dataset ■
Download Flickr2K dataset and place it on
Flickr2K/Flickr2K_HR for train data of 2650 images.
Run CreateTrainingSetAll_Flickr2K.m to create Flickr2K_RGB_MatlabF folder that contains converted mat files.
Run SRGAN_Train.m to train and create trained file.
Use your trained file on SRGAN_2xSuperResolution.m
Other dataset such as DIV2K should be fine to train.
■ Difference from original SRGAN ■
  • Training input image size is 112x112 (not 48x48)
  • Only 2x super resolution is implemented.
  • VGG19_54 loss, MSE loss, and GAN loss weighting ratio for Generator training is different.
■ Training image displayed becomes complete white. How to fix it ■
  • Reduce the learning rate.
  • Set breakpoint at SRGAN_Train.m:353 and run it, read values of lossGenMSE, lossGenFromDisc, lossGenContent value balance on Workspace. If one value is significantly larger than other two, decrease it.
  • First 1000 iteration or so after the training start to use GAN tends to unstable (but it should not become complete white) but the result should be stabilized eventually.
■ How to get more crisp image ■
Decrease lossGenMSE contribution of SRGAN_Train.m:353 to get more crisp image. But artifact increases.
■ Changelog ■
Version 20210703 1.0.0
  • Initial release.
■ References ■
Train Generative Adversarial Network (GAN) using Matlab
Monitor GAN Training Progress and Identify Common Failure Modes
VGG-19 convolutional neural network (Matlab)
Ledig, C., Theis, L., Husz ́ar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken,A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR (2017)
Single Image Super-Resolution Using Deep Learning
(VDSR is implemented using Matlab Deep Learning Toolbox)
Matlab PReLU layer

Cite As

manoreken (2022). SRGAN-VGG54 Single Image Super Resolution Matlab port. (https://www.mathworks.com/matlabcentral/fileexchange/95228-srgan-vgg54-single-image-super-resolution-matlab-port), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2021a
Compatible with R2021a
Platform Compatibility
Windows macOS Linux
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