Dual Autoencoder Network for Retinex-Based Low-Light Image Enhancement

This paper presents a dual autoencoder network model based on the retinex theory to perform the low-light enhancement and noise reduction by combining the stacked and convolutional autoencoders. The proposed method first estimates the spatially smooth illumination component which is brighter than an...

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Veröffentlicht in:	IEEE access Jg. 6; S. 22084 - 22093
Hauptverfasser:	Park, Seonhee, Yu, Soohwan, Kim, Minseo, Park, Kwanwoo, Paik, Joonki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Autoencoder Brightness Convolution Image edge detection Image enhancement image processing Image reconstruction Lighting neural networks Noise reduction unsupervised learning variational retinex model
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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