A self-adaptive single underwater image restoration algorithm for improving graphic quality
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(2020) 2020:41
RESEARCH
EURASIP Journal on Image and Video Processing
Open Access
A self-adaptive single underwater image restoration algorithm for improving graphic quality Herng-Hua Chang* , Po-Fang Chen, Jun-Kai Guo and Chia-Chi Sung * Correspondence: herbertchang@ ntu.edu.tw Department of Engineering Science and Ocean Engineering, National Taiwan University, 1 Sec. 4 Roosevelt Road, Daan, Taipei 10617, Taiwan
Abstract A high-quality underwater image is essential to many industrial and academic applications in the field of image processing and analysis. Unfortunately, underwater images frequently demonstrate poor visual quality of low contrast, blurring, darkness, and color diminishing. This paper develops a new underwater image restoration framework that consists of four major phases: color correction, local contrast enhancement, haze diminution, and global contrast enhancement. A self-adaptive mechanism is designed to guide the image to either processing route based on a red deficiency measure. In the color correction phase, the histogram in each RGB channel is transformed for balancing the image color. An adaptive histogram equalization method is exploited to enhance the local contrast in the CIE-Lab color space. The dark channel prior haze removal scheme is modified for dehazing in the haze diminution phase. Finally, a histogram stretching method is applied in the HSI color space to make the image more natural. A wide variety of underwater images with various scenarios were employed to evaluate this new restoration algorithm. Experimental results demonstrated the effectiveness of our image restoration scheme as compared with state-of-the-art methods. It was suggested that our framework dramatically eliminated the haze and improved visual interpretation of underwater images. Keywords: Underwater image restoration, Image dehazing, Color correction, Haze removal, Dark channel prior
1 Introduction With recent advances in diversified technologies, high-end underwater remotely operated vehicles (ROVs), autonomous underwater vehicles (AUVs), and autonomous underwater robots have been extensively employed for navigation, exploration, and surveillance in underwater environments. These underwater vehicles and robots are typically equipped with optical sensors for acquiring underwater images. From the perspective of academia and industry, underwater imaging is critical to various applications such as archaeology, mine and wreckage detection, marine biology, water fauna identification and assessment, and offshore wind power turbine basis inspection [1, 2]. Nonetheless, the captured images are often degraded with blurring, darkness, low contrast, and color diminishing because of particular propagation properties of light © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source,
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