<p>Deep neural networks have revolutionized the design of image super resolution schemes, in view of their capability of learning suitable features of high-resolution images. The performance of the deep image super resolution networks is very dependent on the distribution of the samples used for their training process. When the deep super resolution networks try to learn super-resolving the low-resolution images from different distributions in a sequential manner, they can only provide high performance for the most recent low-resolution image distribution used in their training process. In view of this, and in order to address the forgetting problem of the super resolution networks during learning from a new distribution of the low-resolution images, in this paper, we propose a continual learning-based scheme, which is developed based on the knowledge distillation technique. Specifically, our proposed deep continual learning-based image super resolution method aims at retaining the knowledge obtained from the previously learned distribution of the training samples, while learning the new distribution as efficiently as possible. To achieve this, the proposed scheme employs the supervision of the signals produced by multiple teacher networks. The results of the extensive experimentation show the effectiveness of the various ideas employed in the development of the proposed method. Further, it is shown that the proposed scheme outperforms the various state-of-the-art image super resolution methods when they are subjected to learning different distributions of the low-resolution images.</p>