The common loss function used for this is the MSE (Mean Squared Error) between the network output patch and the ground truth high-resolution patch..A solution for this loss function is the use of a perceptual loss function developed by Johnson et al..[1]..This loss function is computed by taking the difference in feature map activations in high layers of a VGG network [2] between the network output patch and the high resolution patch..The feature map activations are thus denoted as a perceptual loss metric.GANs further improve the development of this loss function as demonstrated by Ledig et al..[3]..In addition to the perceptual, content loss, an adversarial loss is added to further push images towards a natural image manifold..The GAN framework is integrated to denote if the patch created by the generator is similar to a ground truth set of high-resolution patches..The error of the generator network is then calculated through the adversarial loss, as well as the perceptual loss (denoted by the difference in VGG feature map activation from the outputted patch and ground truth patch).It is very interesting to see how multiple loss functions can be used to optimize neural networks..In this case, each loss function provides a unique perspective on the problem..Thank you for reading, check out the papers below if you are interested in this topic!References:[1] Perceptual Losses for Real-Time Style Transfer and Super-Resolution..Justin Johnson, Alexandre Alahi, Li Fei-Fei.[2]Very Deep Convolutional Networks for Large-Scale Image Recognition.. More details