Underwater image de-scattering and classification by deep neural network. (August 2016)

• Record Type: Journal Article
• Authors: Li, Yujie; Lu, Huimin; Li, Jianru; Li, Xin; Li, Yun; Serikawa, Seiichi
• Is Part Of: Computers & electrical engineering. Volume 54(2016)
• Page Start: 68
• Page End: 77
• Publication Date: 2016-08
• Subjects: Contrast enhancement -- Quality assessment -- Underwater imaging -- Ocean optics -- Deep neural network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.08.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: We have proposed a joint guidance image filter to refine the coarse transmission map that outperforms conventional methods. We have proposed a color correction method restores the scene color correctly. It fully considers illumination lighting and camera spectral characteristics. We have tested that the proposed method can be applied for preprocessing of deep learning-based classification and recognition architecture. We have investigated an underwater image quality assessment index Qu. Abstract: Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. High turbid underwater image enhancement is still an opening issue. Meanwhile, we find that the de-scattering and color correction of underwater images affect classification results. In this paper, we correspondingly propose a novel joint guidance image de-scattering and physical spectral characteristics-based color correction method to enhance high turbidity underwater images. The proposed enhancement method removes the scatter and preserves colors. In addition, as a rule to compare the performance of different image enhancement algorithms, a more comprehensive image quality assessment index Qu is proposed. The index combines the benefits of SSIM index and color distance index. We also use different machine learning methods for classification, such as support vector machine, convolutional neural network. Experimental results show that the proposed approach statistically outperforms state-of-the-art general purpose underwater image contrast enhancement algorithms. The experiment also demonstrated that the proposed method performs well for image classification.