SRNMSM: A Deep Light-Weight Image Super Resolution Network Using Multi-Scale Spatial and Morphological Feature Generating Residual Blocks
Alireza Esmaeilzehi, M. Omair Ahmad, M.N.S. Swamy
Abstract
Generating features representing the textures and structures of an image is very important characteristic of a super resolution network. Morphological operations are the nonlinear mathematical operations that can process signals focusing on their structures and textures. In this paper, we propose a novel residual block to generate and process morphological features and fuse them with the conventional spatial features, in order to produce a very rich and highly representational set of residual feature maps. The proposed residual block is then used in a deep convolutional neural network for the task of image super resolution. It is shown that the capability of the proposed block in generating and using the morphological features can significantly improve the super resolution performance of a deep network. The super resolution network employing the proposed residual block is shown to outperform the state-of-the-art low-complexity image super resolution networks on various benchmark datasets.