  In recent years, limited research has discussed the loss function in the
super-resolution process. The majority of those studies have only used
perceptual similarity conventionally. This is while the development of
appropriate loss can improve the quality of other methods as well. In this
article, a new weighting method for pixel-wise loss is proposed. With the help
of this method, it is possible to use trainable weights based on the general
structure of the image and its perceptual features while maintaining the
advantages of pixel-wise loss. Also, a criterion for comparing weights of loss
is introduced so that the weights can be estimated directly by a convolutional
neural network. In addition, in this article, the expectation-maximization
method is used for the simultaneous estimation super-resolution network and
weighting network. In addition, a new activation function, called "FixedSum",
is introduced which can keep the sum of all components of vector constants
while keeping the output components between zero and one. As experimental
results shows, weighted loss by the proposed method leads to better results
than the unweighted loss and weighted loss based on uncertainty in both
signal-to-noise and perceptual similarity senses on the state-of-the-art
networks. Code is available online.
