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

IEEE Access

Joonki Paik¹  Minseo Kim¹  Kwanwoo Park¹  Seonhee Park¹  Soohwan Yu¹ 

[1] Department of Image Graduate School of Advanced Imaging Science, Multimedia and Film, Chung-Ang University, Seoul, South Korea;

关键词: Autoencoder;    image processing;    image enhancement;    neural networks;    variational retinex model;    unsupervised learning;   

DOI  :  10.1109/ACCESS.2018.2812809

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown