Single underwater image haze removal with a learning-based approach to blurriness estimation

Chen J.; Wu H.-T.; Lu L.Luo X.Hu J.

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Single underwater image haze removal with a learning-based approach to blurriness estimation

机译：Single underwater image haze removal with a learning-based approach to blurriness estimation

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? 2022 Elsevier Inc.Underwater images are usually degraded due to light scattering and absorption. To recover the scene radiance of degraded underwater images, a new haze removal method is presented by incorporating a learning-based approach to blurriness estimation with the image formation model. Firstly, the image blurriness is estimated with a linear model trained on a set of selected grayscale images, the average Gaussian images and blurriness images. With the estimated image blurriness, three intermediate background lights (BLs) are computed to obtain the synthesized BL. Then the scene depth is calculated by using the estimated image blurriness and BL to construct a transmission map and restore the scene radiance. Compared with other haze removal methods, haze in degraded underwater images can be removed more accurately with our proposed method. Moreover, visual inspection, quantitative evaluation and application test demonstrate that our method is superior to the compared methods and beneficial to high-level vision tasks.

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《Journal of visual communication & image representation》 |2022年第11期|1.1-1.10|共10页
作者
Chen J.; Wu H.-T.; Lu L.Luo X.Hu J.;
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School of Computer Science and Engineering South China University of Technology;

School of Computer Science and Engineering South China University of TechnologySchool of Computer Science and Engineering South China University of Technology||Peng Cheng Laboratory;

||School of Computer Science and Engineering South China University of Technology||Peng Cheng LaboratoryState Key Laboratory of Mathematical Engineering and Advanced Computing||Key Laboratory of Cyberspace Situation Awareness of Henan ProvinceSchool of Engineering and Information Technology the University of New South Wales Australian Defence Force Academy;

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正文语种英语
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关键词
Image dehazing; Image enhancement; Image restoration; Underwater image;

Single underwater image haze removal with a learning-based approach to blurriness estimation

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