التفاصيل البيبلوغرافية
| العنوان: |
DehazeNet: An End-to-End System for Single Image Haze Removal |
| المؤلفون: |
Chunmei Qing, Kui Jia, Xiangmin Xu, Bolun Cai, Dacheng Tao |
| بيانات النشر: |
arXiv, 2016. |
| سنة النشر: |
2016 |
| مصطلحات موضوعية: |
FOS: Computer and information sciences, Haze, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Feature extraction, Activation function, Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 020207 software engineering, 02 engineering and technology, Computer Graphics and Computer-Aided Design, Convolutional neural network, Image (mathematics), Nonlinear system, Transmission (telecommunications), 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Computer vision, Artificial Intelligence & Image Processing, Artificial intelligence, business, Software |
| الوصف: |
© 1992-2012 IEEE. Single image haze removal is a challenging ill-posed problem. Existing methods use various constraints/priors to get plausible dehazing solutions. The key to achieve haze removal is to estimate a medium transmission map for an input hazy image. In this paper, we propose a trainable end-to-end system called DehazeNet, for medium transmission estimation. DehazeNet takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model. DehazeNet adopts convolutional neural network-based deep architecture, whose layers are specially designed to embody the established assumptions/priors in image dehazing. Specifically, the layers of Maxout units are used for feature extraction, which can generate almost all haze-relevant features. We also propose a novel nonlinear activation function in DehazeNet, called bilateral rectified linear unit, which is able to improve the quality of recovered haze-free image. We establish connections between the components of the proposed DehazeNet and those used in existing methods. Experiments on benchmark images show that DehazeNet achieves superior performance over existing methods, yet keeps efficient and easy to use. |
| وصف الملف: |
application/pdf |
| DOI: |
10.48550/arxiv.1601.07661 |
| الوصول الحر: |
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::395b729002f69c48399199812255f89fTest |
| حقوق: |
OPEN |
| رقم الانضمام: |
edsair.doi.dedup.....395b729002f69c48399199812255f89f |
| قاعدة البيانات: |
OpenAIRE |
