Machine Learning

Pixel-level self-paced learning for super-resolution

This is an official implementaion of the paper Pixel-level Self-Paced Learning for Super-Resolution, which has been accepted by ICASSP 2020.

[arxiv][PDF]

trained model files: Baidu Pan(code: v0be)

Requirements

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

Core Parts

flow

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


 


 




 


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