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This paper introduces a novel method to simultaneously super-resolve andcolour-predict images acquired by snapshot mosaic sensors. These sensors allowfor spectral images to be acquired using low-power, small form factor,solid-state CMOS sensors that can operate at video frame rates without the needfor complex optical setups. Despite their desirable traits, their main drawbackstems from the fact that the spatial resolution of the imagery acquired bythese sensors is low. Moreover, chromatic mapping in snapshot mosaic sensors isnot straightforward since the bands delivered by the sensor tend to be narrowand unevenly distributed across the range in which they operate. We tackle thisdrawback as applied to chromatic mapping by using a residual channel attentionnetwork equipped with a texture sensitive block. Our method significantlyoutperforms the traditional approach of interpolating the image and,afterwards, applying a colour matching function. This work establishesstate-of-the-art in this domain while also making available to the researchcommunity a dataset containing 296 registered stereo multi-spectral/RGB imagespairs.

Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network