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

• URL: http://arxiv.org/abs/2209.10489v1
• Date: Wed, 21 Sep 2022 16:44:06 GMT
• Title: Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data
• Authors: David O'Callaghan, Cian Ryan, Waseem Shariff, Muhammad Ali Farooq, Joseph Lemley, Peter Corcoran
• Abstract summary: 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 network was able to achieve a mean peak signal to noise ratio of 39.24 on the validation dataset for 4x super-resolution, outperforming bi-cubic both quantitatively and qualitatively.
• Score: 1.7289819674602296
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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