How to modify your code LapSRN_WGAN for same size image processing?

[kirill-pinigin]

Hello. Please help me how to modify your code LapSRN_WGAN for same size image processing without upsampling input image? I want use LapSRN_WGAN for image processing with same size

Should I modify this line of code in https://github.com/twtygqyy/pytorch-LapSRN/blob/master/lapsrn_wgan.py

Change " nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=4, stride=2, padding=1, bias=False)", to nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=1, padding=1, bias=False),

Or may be I can just remove this layer?

[twtygqyy]

Hi @kirill-pinigin, you are right, but you will have to change the size of the input training samples. Check my VDSR repo which same size input and output are used for training.

[kirill-pinigin]

Thank you I understood. I chahge size of input image. But I don't understand what I have to do with nn.ConvTranspose2d layer? Just remove or replace on simple Conv2D?

[twtygqyy]

@kirill-pinigin you can just remove nn.ConvTranspose2d layers.

[kirill-pinigin]

May I ask you? If you do not mind I need to know your expert opinion. Can I use LapSRN_WGAN for image inpainting? I want to eliminate artefacts from grayscale image? What should I do? Can LapSRN_WGAN reduce or even elminate scratches or other artefacts from grayscale photo? What can you advise me?

[twtygqyy]

Hi @kirill-pinigin Of course the code can be modified for image in-painting. Basically there are two steps you need to do:

1. Remove deconvolutional layers, keep input and output in same shape. You can check my VDSR repo for reference.
2. Create input and output training datasets. I guess input could be image with mask, and output should be the raw image.

[kirill-pinigin]

@twtygqyy
I have already used your VDSR model in same maner like iitem 2 . unfortunately qualiy of restored images is not appropriate. May you advise some method to improve perceptual quality of restored images.

May be I should change kernel Size or quantity of layer in VDSR model or may be method of learning rate adjusting?

Or may be I strat to train LapSRN_WGAN in same maner?

[twtygqyy]

@kirill-pinigin I will suggest you to use GAN instead of MSE-loss for training the network. It usually works better for this task. Check out http://hi.cs.waseda.ac.jp/~iizuka/projects/completion/en/