A Novel Two-stage Residual Learning Based Convolutional Neural Network for Image Super Resolution

Image super resolution has gained a lot of attention due to its applications in different fields of image processing. It is used to produce high-resolution images from low-resolution input. Because of the excellent learning capability of convolution neural networks, these networks are able to learn complex spatial structures for image super-resolution. In this paper, two different architectures have been proposed for image super resolution. The first architecture is Dual Subpixel Layer Convolution Neural Network (DSL-CNN), which stacks two subpixel CNN architectures to enhance model depth for better representational capability. Two stages provide an effective upscaling factor of 4. In the second architecture, named as Residue based Dual Subpixel Layer Convolution Neural Network (RDSL-CNN), two-stage residual learning has been introduced which effectively sustains the high frequency details and provides superior results than the previous state-of-the-art methods. The performance of the two architectures has been evaluated on various image datasets, and compared with other state-of-the-art methods.