  Thermal imaging has numerous advantages over regular visible-range imaging
since it performs well in low-light circumstances. Super-Resolution approaches
can broaden their usefulness by replicating accurate high-resolution thermal
pictures using measurements from low-cost, low-resolution thermal sensors.
Because of the spectral range mismatch between the images, Guided
Super-Resolution of thermal images utilizing visible range images is difficult.
However, In case of failure to capture Visible Range Images can prevent the
operations of applications in critical areas. We present a novel data fusion
framework and regularization technique for Guided Super Resolution of Thermal
images. The proposed architecture is computationally in-expensive and
lightweight with the ability to maintain performance despite missing one of the
modalities, i.e., high-resolution RGB image or the lower-resolution thermal
image, and is designed to be robust in the presence of missing data. The
proposed method presents a promising solution to the frequently occurring
problem of missing modalities in a real-world scenario. Code is available at
https://github.com/Kasliwal17/CoReFusion .
