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Multi-Scale Discriminative Feature Loss

This repository provides code for Multi-Scale Discriminative Feature (MDF) loss for image reconstruction algorithms.

Description

Central to the application of neural networks in image restoration problems, such as single image super resolution, is the choice of a loss function that encourages natural and perceptually pleasing results. We provide a lightweight feature extractor that outperforms state-of-the-art loss functions in single image super resolution, denoising, and JPEG artefact removal. We propose a novel Multi-Scale Discriminative Feature (MDF) loss comprising a series of discriminators, trained to penalize errors introduced by a generator. For further information please refer to the project webpage.

Usage

The code runs in Python3 and Pytorch.

First install the dependencies by running:

pip3 install -r requirements.txt

To run a simple example, optimizing image pixels:

import torch as pt
import torch.optim as optim
import imageio
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import Variable

from mdfloss import MDFLoss


# Set parameters
cuda_available = False
epochs = 25
application = 'Denoising'
image_path = './misc/i10.png'

if application =='SISR':
    path_disc = "./weights/Ds_SISR.pth"
elif application == 'Denoising':
    path_disc = "./weights/Ds_Denoising.pth"
elif application == 'JPEG':
    path_disc = "./weights/Ds_JPEG.pth"

# Read reference images
imgr = imageio.imread(image_path)
imgr = pt.from_numpy(imageio.core.asarray(imgr/255.0))
imgr = imgr.type(dtype=pt.float64)
imgr = imgr.permute(2,0,1)
imgr = imgr.unsqueeze(0).type(pt.FloatTensor)

# Create a noisy image 
imgd = pt.rand(imgr.size())

if cuda_available:
    imgr = imgr.cuda()
    imgd = imgd.cuda()

# Convert images to variables to support gradients
imgrb = Variable( imgr, requires_grad = False)
imgdb = Variable( imgd, requires_grad = True)

optimizer = optim.Adam([imgdb], lr=0.1)

# Initialise the loss
criterion = MDFLoss(path_disc, cuda_available=cuda_available)

# Iterate over the epochs optimizing for the noisy image
for ii in range(0,epochs):
    
    optimizer.zero_grad()
    loss = criterion(imgrb,imgdb) 
    print("Epoch: ",ii," loss: ", loss.item())
    loss.backward()
    optimizer.step()

Citing

If using, please cite:

@inproceedings{mustafa2022training,
  title={Training a Task-Specific Image Reconstruction Loss},
  author={Mustafa, Aamir and Mikhailiuk, Aliaksei and Iliescu, Dan Andrei and Babbar, Varun and Mantiuk, Rafa{\l} K},
  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
  pages={2319--2328},
  year={2022}
}

Acknowledgement

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement N◦ 725253–EyeCode).