ESRGAN Super Resolution Camera Raw De-Bayering

バージョン 1.0.1 (8.47 MB) 作成者: manoreken
ESRGAN Super Resolution Camera Raw De-Bayering. Performs Camera Raw image de-Bayer using deep learning.

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更新 2022/6/11

ライセンスの表示

ESRGAN Super Resolution Camera Raw De-Bayer version 1.0.0.
■ Prerequisites ■
Matlab 2022a
Image Processing toolbox
Statistics and Machine Learning toolbox
Deep Learning Toolbox
Parallel Computing Toolbox
■ How to Test ■
Run ESRGAN_Test.m which calls ESRGAN_2xSuperResolution.m
Trained net is loaded on the line 5 of ESRGAN_2xSuperResolution.m
■ How to Perform ESRGAN Super-Resolution to your image file ■
Input Camera RAW image should be RG/GB Bayer pattern layout. Otherwise it is necessary to re-train the network.
Raw image format should be supported by Matlab. It is possible to convert your Camera RAW image to
DNG file format using Adobe DNG Converter (Free of charge) https://helpx.adobe.com/camera-raw/using/adobe-dng-converter.html
Specify your file to filename variable on line 1 of ESRGANRaw_DebayerTest.m and Run it.
After 10 minutes or so, developed.png will be outputted.
■ Limitations ■
・Lens chromatic abberation is not corrected.
・Image brightness may be not correct.
■ How to Train the network ■
Download Flickr2K dataset and place all png files on Flickr2K/Flickr2K_HR.
Run createTrainingSetAll_Flickr2K.m to create Flickr2K_RGB_MatlabF2 folder that contains converted mat files.
Set channel='R' in line 17 and run ESRGANRawRB_Train.m
Set channel='B' in line 17 and run ESRGANRawRB_Train.m
Run ESRGanRawG_Train.m
■ My training result becomes complete white image. How to fix it ■
・Reduce the learning rate.
・Run ESRGAN_Train.m and watch values of lossGenMSE, lossGenFromDisc, lossGenVGG54 on Command Window.
If one value is significantly larger than other two, decrease it.
■ How to get more crisp image ■
Decrease lossGenMSE contribution of ESRGAN_Train.m:399 to get more crisp image. But artifact increases.
■ Changelog ■
Version 1.0.1
・Fixed G-channel offset bug
Version 1.0.0
・Initial release.
■ References ■
Implement Digital Camera Processing Pipeline
https://www.mathworks.com/help/images/end-to-end-implementation-of-digital-camera-processing-pipeline.html
Xintao Wang, et al. ESRGAN: Enhanced super-resolution generative adversarial networks. In ECCVW, 2018.
https://arxiv.org/abs/1809.00219
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)
https://arxiv.org/pdf/1609.04802.pdf
Single Image Super-Resolution Using Deep Learning
(VDSR is implemented using Matlab Deep Learning Toolbox)
https://www.mathworks.com/help/images/single-image-super-resolution-using-deep-learning.html
Train Generative Adversarial Network (GAN) using Matlab
https://www.mathworks.com/help/deeplearning/ug/train-generative-adversarial-network.html
Monitor GAN Training Progress and Identify Common Failure Modes
https://www.mathworks.com/help/deeplearning/ug/monitor-gan-training-progress-and-identify-common-failure-modes.html
VGG-19 convolutional neural network (Matlab)
https://www.mathworks.com/help/deeplearning/ref/vgg19.html?searchHighlight=VGG19&s_tid=srchtitle

引用

manoreken (2022). ESRGAN Super Resolution Camera Raw De-Bayering (https://www.mathworks.com/matlabcentral/fileexchange/112350-esrgan-super-resolution-camera-raw-de-bayering), MATLAB Central File Exchange. 取得済み .

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