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

• Published in: arXiv.org
• Main Authors: O'Callaghan, David, Ryan, Cian, Shariff, Waseem, Muhammad Ali Farooq, Lemley, Joseph, Corcoran, Peter
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 21.09.2022
• Subjects: Algorithms; Artificial neural networks; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Data acquisition; Heat detection; Image enhancement; Image resolution; Indoor environments; Interpolation; Machine learning; Neural networks; Recurrent neural networks; Signal processing; Signal to noise ratio; Thermal imaging
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2209.10489

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.