TOWARDS UNSUPERVISED SINGLE IMAGE DEHAZING WITH DEEP LEARNING

被引：0

作者：

Huang, Lu-Yao ^{[1
,2
]}

Yin, Jia-Li ^{[2
]}

Chen, Bo-Hao ^{[2
]}

Ye, Shao-Zhen ^{[1
]}

机构：

[1] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350116, Peoples R China

[2] Yuan Ze Univ, Dept Comp Sci & Engn, Taoyuan 320, Taiwan

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2019年

基金：

中国国家自然科学基金;

关键词：

Image dehazing; unsupervised learning; transmission estimation;

D O I：

10.1109/icip.2019.8803316

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

Deep learning computation is often used in single-image dehazing techniques for outdoor vision systems. Its development is restricted by the difficulties in providing a training set of degraded and ground-truth image pairs. In this paper, we develop a novel model that utilizes cycle generative adversarial network through unsupervised learning to effectively remove the requirement of a haze/depth data set. Qualitative and quantitative experiments demonstrated that the proposed model outperforms existing state-of-the-art dehazing models when tested on both synthetic and real haze images.

引用

页码：2741 / 2745

页数：5

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