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arXiv · 2209.10489

Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data

David O'Callaghan·Cian Ryan·Waseem Shariff·Muhammad Ali Farooq·Joseph Lemley·Peter Corcoran
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Abstract

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.

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David O'Callaghan, Cian Ryan, Waseem Shariff, Muhammad Ali Farooq, Joseph Lemley, Peter Corcoran. 2022-09-21. Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data. https://doi.org/10.56541/uaov9084

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