LLCNN: A Convolutional Neural Network for Low-light Image Enhancement

被引：0

作者：

Tao, Li ^{[1
]}

Zhu, Chuang ^{[1
]}

Xiang, Guoqing ^{[1
]}

Li, Yuan ^{[1
]}

Jia, Huizhu ^{[1
,2
]}

Xie, Xiaodong ^{[1
,2
]}

机构：

[1] Peking Univ, Natl Engn Lab Video Technol, Beijing, Peoples R China

[2] Cooperat Medianet Innovat Ctr & Beida Binhai Info, Tianjin, Peoples R China

来源：

2017 IEEE VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP) | 2017年

基金：

美国国家科学基金会;

关键词：

low-light image; contrast enhancement; deep learning; CNN; SSIM loss; HISTOGRAM EQUALIZATION; QUALITY ASSESSMENT;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a CNN based method to perform low-light image enhancement. We design a special module to utilize multiscale feature maps, which can avoid gradient vanishing problem as well. In order to preserve image textures as much as possible, we use SSIM loss to train our model. The contrast of low-light images can be adaptively enhanced using our method. Results demonstrate that our CNN based method outperforms other contrast enhancement methods.

引用

页数：4

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