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Recurrent Super-Resolution Method for Enhancing Low Quality...

Author
O'Callaghan, David, Ryan, Cian, Shariff, Waseem, Ali Farooq, Muhammad, Lemley, Joseph, Corcoran, Peter
Category
cs.CV
Date Published
2022/09/21
Date Retrieved
2022/09/22
Date Updated
2022/09/22
Description
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.
Posts
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Score
0.75
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URL
http://dx.doi.org/10.56541/uaov9084
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