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Open AccessArticle

Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net

Department of Computer Science and Engineering, Inha University, Incheon 22212, Korea
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Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 495; https://doi.org/10.3390/s20020495
Received: 6 December 2019 / Revised: 4 January 2020 / Accepted: 10 January 2020 / Published: 15 January 2020
(This article belongs to the Special Issue Smart City and Smart Infrastructure)
Low-light image enhancement is one of the most challenging tasks in computer vision, and it is actively researched and used to solve various problems. Most of the time, image processing achieves significant performance under normal lighting conditions. However, under low-light conditions, an image turns out to be noisy and dark, which makes subsequent computer vision tasks difficult. To make buried details more visible, and reduce blur and noise in a low-light captured image, a low-light image enhancement task is necessary. A lot of research has been applied to many different techniques. However, most of these approaches require much effort or expensive equipment to perform low-light image enhancement. For example, the image has to be captured in a raw camera file in order to be processed, and the addressing method does not perform well under extreme low-light conditions. In this paper, we propose a new convolutional network, Attention U-net (the integration of an attention gate and a U-net network), which is able to work on common file types (.PNG, .JPEG, .JPG, etc.) with primary support from deep learning to solve the problem of surveillance camera security in smart city inducements without requiring the raw image file from the camera, and it can perform under the most extreme low-light conditions. View Full-Text
Keywords: surveillance camera security; smart city; low-light image enhancement; attention U-net; deep learning surveillance camera security; smart city; low-light image enhancement; attention U-net; deep learning
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Ai, S.; Kwon, J. Extreme Low-Light Image Enhancement for Surveillance Cameras Using Attention U-Net. Sensors 2020, 20, 495.

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