Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148091
Title: Learning to see in the dark
Authors: Chen, Sihao
Keywords: Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Chen, S. (2021). Learning to see in the dark. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148091
Abstract: Low-light image enhancement aims to improve the visibility of images taken in low-light or nighttime conditions. Currently, most deep models are trained using synthetic low-light datasets or manually collected datasets with small sizes, which limits their generalization capability when encountering the low-light images captured in the wild. In this study, a domain adaptation framework is proposed to translate images between synthetic low-light images and real low-light images. Meanwhile, we embed a method into the proposed domain adaptation framework to generate low-light images of different brightness levels, which helps with the training process of low-light enhancement networks via data augmentation. Finally, an attention-guided U-Net is trained on the augmented dataset. Qualitative and quantitative evaluations show that our method is comparable to other state-of-the-art methods.
URI: https://hdl.handle.net/10356/148091
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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