This paper introduces a novel method to super-resolve multi-spectral images captured by modern real-time single-shot mosaic image sensors, also known as multi-spectral cameras. Our contribution is two-fold. Firstly, we super-resolve multi-spectral images from mosaic images rather than image cubes, which helps to take into account the spatial offset of each wavelength. Secondly, we introduce an external multi-scale feature aggregation network (Multi-FAN) which concatenates the feature maps with different levels of semantic information throughout a super-resolution (SR) network. A cascade of convolutional layers then implicitly selects the most valuable feature maps to generate a mosaic image. This mosaic image is then merged with the mosaic image generated by the SR network to produce a quantitatively superior image. We apply our Multi-FAN to RCAN (Residual Channel Attention Network), which is the state-of-the-art SR algorithm. We show that Multi-FAN improves both quantitative results and well as inference time.
@article{arxiv.1909.07577,
title = {Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network},
author = {Mehrdad Shoeiby and Sadegh Aliakbarian and Saeed Anwar and Lars Petersson},
journal= {arXiv preprint arXiv:1909.07577},
year = {2019}
}