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This paper introduces a novel method to super-resolve multi-spectral imagescaptured by modern real-time single-shot mosaic image sensors, also known asmulti-spectral cameras. Our contribution is two-fold. Firstly, we super-resolvemulti-spectral images from mosaic images rather than image cubes, which helpsto take into account the spatial offset of each wavelength. Secondly, weintroduce an external multi-scale feature aggregation network (Multi-FAN) whichconcatenates the feature maps with different levels of semantic informationthroughout a super-resolution (SR) network. A cascade of convolutional layersthen implicitly selects the most valuable feature maps to generate a mosaicimage. This mosaic image is then merged with the mosaic image generated by theSR network to produce a quantitatively superior image. We apply our Multi-FANto RCAN (Residual Channel Attention Network), which is the state-of-the-art SRalgorithm. We show that Multi-FAN improves both quantitative results and wellas inference time.

Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network