GCResNet
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.
The code will be published soon.
PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021.
Overview
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Comments
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Training patch size problem
opened by XiaoBuL 6Hello, I'm confused about the patch size 240 you set in options.xml, while patch size 256 in the paper? If I want to train with 256x256 patch size, is it any impact?
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Questions about training
opened by STQ-AmadeusUser 0Hello! I want to train your GCResNet for image deblurring. The options.xml that i utilize is default in github. I wonder how much the l1 loss will redeuce to if i use GoPro dataset after training 1000 epoches.
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Little question
opened by w-z-hub 3Hello, this is an excellent job. When I was reading your paper, I didn’t quite understand the setting of degree in the article. Did you change the setting in WattsStrogatz.m and adjacency.m to make the graph you need. Another question is whether the three data of full.mat, fullSR.mat, and sr64.mat correspond to the cases where the number of channels is 96, 128, and 64. I'm looking forward to your answer.Thank you.
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a little question
opened by PASSENGER128 1Hello, this is an excellent job. When I was reading your paper, I had a question: You do graph convolution network on the feature space and it feels similar to channel attention. Is there any difference between them? In other words, how is doing Graph convolution in feature space better than channel attention?
I'm looking forward to your answer.Thank you.
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