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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Bibliographic Details
Published in:IEEE access Vol. 6; pp. 22084 - 22093
Main Authors: Park, Seonhee, Yu, Soohwan, Kim, Minseo, Park, Kwanwoo, Paik, Joonki
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 input low-light image using a stacked autoencoder with a small number of hidden units. Next, we use a convolutional autoencoder which deals with 2-D image information to reduce the amplified noise in the brightness enhancement process. We analyzed and compared roles of the stacked and convolutional autoencoders with the constraint terms of the variational retinex model. In the experiments, we demonstrate the performance of the proposed algorithm by comparing with the state-of-the-art existing low-light and contrast enhancement methods.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2812809