Zhe Wang1, Liguo Zhang1,2,*, Tong Shuai3, Shuo Liang3, Sizhao Li1,4
Journal of New Media, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jnm.2022.027826
Abstract Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality. However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise. Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult. In this paper, we propose a deep learning method for single image super-resolution. The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images. Finally, these features… More >