Edge-aware and edge-enhancing single digital image super-resolution using deep learning
Deep learning based single image super resolution has been researched for a while now. However, the ability to construct a higher resolution image with better structural integrity at the edges is yet to be reached for a given low resolution image. This inability occurs mainly due to treating the texture and edges alike at the design and training phase of the neural networks. This paper tries to address these issues by taking both the textural features and the edge features into consideration at both the neural network design level and training level. These considerations not only achieved qualitatively better high resolution images, but the neural network model involved is also lighter in terms of number of parameters and multiply and accumulation operations when compared competing state of the art deep learning based super resolution approaches.