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CLC number: TP391.41

On-line Access: 2019-07-08

Received: 2017-11-10

Revision Accepted: 2018-07-12

Crosschecked: 2019-06-11

Cited: 0

Clicked: 1677

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Fei Yuan

http://orcid.org/0000-0002-8614-8756

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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.6 P.862-871

http://doi.org/10.1631/FITEE.1700744


De-scattering and edge-enhancement algorithms for underwater image restoration


Author(s):  Pan-wang Pan, Fei Yuan, En Cheng

Affiliation(s):  MOE Key Laboratory of Underwater Acoustic Communication and Marine Information Technology, Xiamen University, Xiamen 361005, China; more

Corresponding email(s):   yuanfei@xmu.edu.cn

Key Words:  Image de-scattering, Edge enhancement, Convolutional neural network, Non-subsampled contourlet transform


Pan-wang Pan, Fei Yuan, En Cheng. De-scattering and edge-enhancement algorithms for underwater image restoration[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(6): 862-871.

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T1 - De-scattering and edge-enhancement algorithms for underwater image restoration
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J0 - Frontiers of Information Technology & Electronic Engineering
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Abstract: 
Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

基于去散射与边缘增强算法的水下图像复原

摘要:对色差严重和边缘模糊的水下图像需进行复原。一般分两步:去散射和边缘增强。首先,提出一种用于水下图像去散射的多尺度迭代框架。利用卷积神经网络估计传输图,再用自适应双边滤波器改进传输图估计结果。由于无可用数据集训练网络,收集包含2000个水下图像的数据集以获得合成数据。其次,采用白平衡算法消除水下图像的色偏。最后将图像转换到特殊变换域,使用非下采样轮廓波变换对边缘去噪和增强。结果表明:该方法主、客观质量均明显优于现有方法。

关键词:图像散射;边缘增强;卷积神经网络;非下采样轮廓波变换

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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