A Fast, Simple, and Flexible Scale Informative Feature Transform Module for Arbitrary Scale Image Super-Resolution

The single-image super-resolution domain has witnessed a significant performance improvement due to the advancement of deep learning models. However, most of the deep learning models use an integer-scale and scale-specific model for super-resolution. Separate scale-specific networks require huge memory during deployment. Therefore, a single model for any random scale image super-resolution is an old age demand. Unlike existing solutions based on implicit representation functions, we propose a fully convolutional arbitrary scale upscaling module. Our proposed module consists of fewer parameters and consumes less memory and inference time than existing ones. As it is based on a simple convolutional neural network, it has the flexibility to be adapted to any other networks for arbitrary scale transformation. We also show that the proposed upscaling module can be extended to super-resolution under homographic transformation. We perform extensive experiments on widely used benchmark datasets, and experimental findings show the comparative performance of our proposed upscaling module as compared to recently developed approaches, while it provides ad-hoc benefits of being simple and computationally inexpensive.