Towards Perceptually Plausible Training of Image Restoration Neural Networks

Learning-based black-box approaches have proven to be successful at several tasks in image and video processing domain. Many of these approaches depend on gradient-descent and back-propagation algorithms which requires to calculate the gradient of the loss function. However, many of the visual metrics are not differentiable, and despite their superior accuracy, they cannot be used to train neural networks for imaging tasks. Most of the image restoration neural networks rely on mean squared error to train. In this paper, we investigate visual system based metrics in order to provide perceptual loss functions that can replace mean squared error for gradient descent-based algorithms. We also share our preliminary results on the proposed approach.