Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks

High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.