  The process of obtaining high-resolution images from single or multiple
low-resolution images of the same scene is of great interest for real-world
image and signal processing applications. This study is about exploring the
potential usage of deep learning based image super-resolution algorithms on
thermal data for producing high quality thermal imaging results for in-cabin
vehicular driver monitoring systems. In this work we have proposed and
developed a novel multi-image super-resolution recurrent neural network to
enhance the resolution and improve the quality of low-resolution thermal
imaging data captured from uncooled thermal cameras. The end-to-end fully
convolutional neural network is trained from scratch on newly acquired thermal
data of 30 different subjects in indoor environmental conditions. The
effectiveness of the thermally tuned super-resolution network is validated
quantitatively as well as qualitatively on test data of 6 distinct subjects.
The network was able to achieve a mean peak signal to noise ratio of 39.24 on
the validation dataset for 4x super-resolution, outperforming bicubic
interpolation both quantitatively and qualitatively.
