Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement

Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adju...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 31; pp. 3997 - 4010
Main Authors: Zhang, Weidong, Zhuang, Peixian, Sun, Hai-Han, Li, Guohou, Kwong, Sam, Li, Chongyi
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Attenuation
Channel estimation
Color
color correction
contrast enhancement
Degradation
Electromagnetic absorption
Image color analysis
Image contrast
Image enhancement
Image segmentation
Imaging
Learning systems
light scattering
Low visibility
Underwater
Underwater image enhancement
underwater imaging
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size <inline-formula> <tex-math notation="LaTeX">1024\times 1024 \times 3 </tex-math></inline-formula> on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2022.3177129