Infrared image super-resolution via discriminative dictionary and deep residual network.

• Proposing a novel method benefiting from both compressive sensing and deep learning. • Introducing a discriminative dictionary learning method to better characterize images. • Generating a more accurate input interpolated low-resolution image for neural network. • Constructing a deep residual network to capture more high-frequency information. Image super-resolution successfully overcomes the limitation of infrared imaging system and satisfies the increasing demand for high-resolution infrared images. In this paper, we propose a novel infrared image super-resolution method via discriminative dictionary and deep residual network, which is benefited from the advantages of both compressive sensing and deep learning for more accurate reconstruction results. A discriminative dictionary learning method is introduced to explicitly learn a shared sub-dictionary with a set of cluster-specific sub-dictionaries from the training dataset to better characterize the specific representation of each image. Besides, multiple constraints are integrated to preserve the intrinsic relationship and structure information among images in the learned dictionary. Moreover, to further compensate for the limitation of dictionary representation capacity, a deep residual network is constructed to capture more high-frequency details of infrared images. Both quantitative and qualitative evaluations demonstrate that the proposed method could achieve favorable performances against other state-of-the-art methods. [ABSTRACT FROM AUTHOR]