In machine learning, transferring and generalizing the knowledge learned from one domain to another is one of the important and basic capabilities. Since supervised learning is not complete, the use of other methods, such as self-supervised learning methods, can be very helpful in domain generalization. In this paper, we present a method that, in addition to classify original images in order to learn data labels in a supervised process, attempts to classify images resulting from the application of discrete wavelet transform on the original images by generating pseudo-labels for them. This extra work as a self-supervision task can lead to learn useful features and a general image representation for images of different domains, which can greatly help to improve the problem of domain generalization. In the following, by combining self-supervised methods such as jigsaw puzzles and guessing the rotation angle with discrete wavelet transform, we show that this combination can improve the results for the domain generalization problem. In this paper, we used the well-known PACS, VLCS and office-Home datasets to perform experiments, and the results show that our proposed method can work better than advanced and state-of-the-art domain generalization methods.