Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140390
Title: Unsupervised deep thermal RGB fusion for home applications with PyTorch framework
Authors: Liu, Yan
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: A3221-191
Abstract: With the development of the artificial intelligence, smart home has been introduced to our daily life. Under this thesis, thermal sensors are widely used for various applications. A low-resolution (LR) thermal sensor can only detect the object’s temperature while a high-resolution (HR) one can show how the temperature spreads on the object. However, the price and the feasibility of the thermal sensors are quite resolution dependent. A good thermal sensor that can monitor the temperature distribution are usually at high costs, which makes it less worthy to be applied in the smart home applications. This project proposes an unsupervised cross-domain guided image super-resolution method to enlarge the thermal resolution with the guided of HR RGB images. We also propose a new loss function to learn from the content of the LR thermal image and preserve the edge details from the high resolution RGB image. We conduct experiment and the results show that the proposed method produces good HR images with comparable quality as the costly HR thermal devices. A prototype is also built to demonstrate its performance.
URI: https://hdl.handle.net/10356/140390
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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