CLC number: TP391.41
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-06-11
Cited: 0
Clicked: 6850
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.
@article{title="De-scattering and edge-enhancement algorithms for underwater image restoration",
author="Pan-wang Pan, Fei Yuan, En Cheng",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="6",
pages="862-871",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700744"
}
%0 Journal Article
%T De-scattering and edge-enhancement algorithms for underwater image restoration
%A Pan-wang Pan
%A Fei Yuan
%A En Cheng
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 6
%P 862-871
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700744
TY - JOUR
T1 - De-scattering and edge-enhancement algorithms for underwater image restoration
A1 - Pan-wang Pan
A1 - Fei Yuan
A1 - En Cheng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 6
SP - 862
EP - 871
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700744
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.
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