This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resolution, which has been accepted by ICASSP 2020.
trained model files: Baidu Pan(code: v0be)
This code is forked from thstkdgus35/EDSR-PyTorch. In the light of its README, following libraries are required:
- Python 3.6+ (Python 3.7.0 in my experiments)
- PyTorch >= 1.0.0 (1.1.0 in my experiments)
- numpy
- skimage
- imageio
- matplotlib
- tqdm
Detail code can be found in Loss.forward, which can be simplified as:
# take L1 Loss as example
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import pytorch_ssim
class Loss(nn.modules.loss._Loss):
def __init__(self, spl_alpha, spl_beta, spl_maxVal):
super(Loss, self).__init__()
self.loss = nn.L1Loss()
self.alpha = spl_alpha
self.beta = spl_beta
self.maxVal = spl_maxVal
def forward(self, sr, hr, step):
# calc sigma value
sigma = self.alpha * step + self.beta
# define gauss function
gauss = lambda x: torch.exp(-((x+1) / sigma) ** 2) * self.maxVal
# ssim value
ssim = pytorch_ssim.ssim(hr, sr, reduction='none').detach()
# calc attention weight
weight = gauss(ssim).detach()
nsr, nhr = sr * weight, hr * weight
# calc loss
lossval = self.loss(nsr, nhr)
return lossvalthe library pytorch_ssim is focked from Po-Hsun-Su/pytorch-ssim and rewrite some details for adopting it to our requirements.
Attention weight values change according to SSIM Index and steps:
If you find this project useful for your research, please cite:
@inproceedings{lin2020pixel,
title={Pixel-Level Self-Paced Learning For Super-Resolution}
author={Lin, Wei and Gao, Junyu and Wang, Qi and Li, Xuelong},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
year={2020},
pages={2538-2542}
}
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