Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data
This work addresses the need for high-quality thermal imaging in vehicular driver monitoring systems, representing an incremental improvement with domain-specific application.
The authors tackled the problem of enhancing low-resolution thermal facial images for in-cabin driver monitoring by proposing a novel multi-image super-resolution recurrent neural network, achieving a mean PSNR of 39.24 for 4x super-resolution and outperforming bicubic interpolation.
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.