@inproceedings{9e54c68d4f5e4ca09c9a79d18d3e6b74,
title = "Low-Resolution Image Enhancement using Generative Adversarial Networks",
abstract = "Enhancing low-resolution (LR) images is crucial in the field of machine vision science. Improving the quality of LR images captured by security cameras is indispensable for forensic analysis and identification in surveillance applications. Generative Adversarial Networks (GANs) have emerged as a powerful deep-learning technique to address the super-resolution (SR) challenge inherent in long-range surveillance photos. The primary aim of this study is to enhance low-quality images of highway security surveillance using GANs architecture to optimize the image quality by generating high-resolution (HR) equivalents of the real HR images. The desired images were reconstructed using a dataset and employing Red-Green-Blue (RGB) guided thermal SR-GANs and GANs, along with perceptual loss function techniques. These approaches enhance image quality while preserving important LR image features, thereby reducing blur and glitches associated with image upscaling.",
keywords = "blur, forensic analysis, image enhancement, low-resolution image, Super-Resolution Generative Adversarial Network (SRGAN)",
author = "Melvin Ajuluchukwu and Atef Shalan and Lei Chen and Yiming Ji and Emmanuel Balogun",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024 ; Conference date: 24-07-2024 Through 26-07-2024",
year = "2024",
doi = "10.1109/AIoT63253.2024.00049",
language = "English",
series = "Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "213--218",
booktitle = "Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024",
address = "United States",
}