codes for IKM (arXiv2021, Submitted to IEEE Trans)

Yuanfei Huang

Last update: Dec 29, 2022

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Deep Learning IKM

Overview

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution

This repository is for IKM introduced in the following paper

Yuanfei Huang, Jie Li, Yanting Hu, Hua Huang and Xinbo Gao*, "Image-specific Convolutional Kernel Modulation for Single Image Super-resolution", arXiv preprint arXiv:2111.08362(2021)

arXiv

Overflow

Dependenices

python 3.8
pytorch >= 1.7.0
NVIDIA GPU + CUDA

Data preparing

Download DIV2K and Flickr2K datasets into the path "../../Datasets/Train/DF2K".

Train

Replace the train dataset path '../../Datasets/Train/' and validation dataset '../../Datasets/Test/' with your training and validation datasets, respectively.
Set the configurations in 'option.py' as you want.

python main.py --train 'Train'

Test

Download models from Google Drive or BaiduYun(password: 06v3).
Replace the test dataset path '../../Datasets/Test/' with your datasets.

python main.py --train 'Test'

Results

Quantitative Results (PSNR/SSIM)

Qualitative Results

Citation

@misc{huang2021imagespecific,
      title={Image-specific Convolutional Kernel Modulation for Single Image Super-resolution}, 
      author={Yuanfei Huang and Jie Li and Yanting Hu and Xinbo Gao and Hua Huang},
      year={2021},
      eprint={2111.08362},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

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Comments

training problem

Hi,thanks for ur work. I got a issue when I tried to apply ur method to my model: RuntimeError: Function CudnnConvolutionBackward returned an invalid gradient at index 1 - got [96, 32, 3, 3] but expected shape compatible with [192, 32, 3, 3] I already change the optimization to IsO, can u help to figure it out please?

opened by wwang0107 0

codes for IKM (arXiv2021, Submitted to IEEE Trans)

Related tags

Overview

Image-specific Convolutional Kernel Modulation for Single Image Super-resolution

Overflow

Dependenices

Data preparing

Train

Test

Results

Quantitative Results (PSNR/SSIM)

Qualitative Results

Citation

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