Low Complexity Underwater Image Enhancement Based on Dark Channel Prior

Yang, Hung‐Yu; Chen, Pei-Yin; Huang, Chien-Chuan; Zhuang, Ya-Zhu; Shiau, Yeu-Horng

doi:10.1109/ibica.2011.9

Cited by 147 publications

(102 citation statements)

References 10 publications

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“…Figures 8-10 show the original images and the images enhanced by different algorithms. More specifically, images in column (a) are the original images, the images in column (b) are created by the original dark channel prior model (DCP) [23,24], the images in column (c) are based on the back-scattering dark channel prior model (BSDCP) [27], and the images in column (d) are produced by our method. As can be seen from the first row of Figure 8, the differences between the results obtained by individual algorithms are very small, as the light is relatively uniform and adequate.…”

Section: Resultsmentioning

confidence: 99%

“…As we have mentioned above, the enhanced images could be over-exposed if we use the original dark channel prior model [23,24]. This is because the reflection underwater and the reflection on land are different.…”

Section: Underwater Image Denoising Algorithmmentioning

confidence: 99%

“…Yang et al [24] proposed an underwater dehaze algorithm based on the dark channel prior model. The overall steps were not changed, and white balance color recovery was added.…”

Section: Underwater Dark Channel Prior Modelsmentioning

confidence: 99%

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An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

Xie

Pan

2018

Robotics

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There are many tasks that require clear and easily recognizable images in the field of underwater robotics and marine science, such as underwater target detection and identification of robot navigation and obstacle avoidance. However, water turbidity makes the underwater image quality too low to recognize. This paper proposes the use of the dark channel prior model for underwater environment recognition, in which underwater reflection models are used to obtain enhanced images. The proposed approach achieves very good performance and multi-scene robustness by combining the dark channel prior model with the underwater diffuse model. The experimental results are given to show the effectiveness of the dark channel prior model in underwater scenarios.

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Section: Resultsmentioning

confidence: 99%

Section: Underwater Image Denoising Algorithmmentioning

confidence: 99%

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An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

Xie

Pan

2018

Robotics

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“…Recently, several techniques have been proposed to handle single underwater image [5,7,8]. In [7,8], the authors directly applied the dark channel prior [4] in underwater conditions.…”

Section: Introductionmentioning

confidence: 99%

Single underwater image enhancement with a new optical model

Wen

Huang

Gao

2013

2013 IEEE International Symposium on Circuits and Systems (ISCAS2013)

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Abstract-As light is attenuated when disseminating in water, the clarity of images or videos captured under water is usually degraded to varying degrees. By exploring the difference in light attenuation between in atmosphere and in water, we derive a new underwater optical model to describe the formation of an underwater image in the true physical process, and then propose an effective enhancement algorithm with the derived optical model to improve the perception of underwater images or video frames. In our algorithm, a new underwater dark channel is derived to estimate the scattering rate, and an effective method is also presented to estimate the background light in the underwater optical model. Experimental results show that our algorithm can well handle underwater images, especially for deep-sea images and those captured from turbid waters.

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“…Underwater Image Haze Removal strengthens and improves the performance of various vision algorithms, increase the visibility of the scene, improves the contrast and quality of underwater images. Various underwater image enhancement techniques [13], [14], [15] have also been developed by researchers to improve the quality and contrast of underwater images.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

Talwar¹,

Kochher²

2015

IJCA

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Underwater images are used to explore the unique life and the world that exists under the water. These images have less clarity, diminishing colors, low contrast .All these issues are a result of haze in underwater images. So, Underwater image haze removal algorithms become very important and necessary for many vision applications. In this paper, after a brief overview of existing methods of haze removal and discovering their limitations, we present a novel technique of underwater image haze removal based on Fuzzy Based DCP and AHE. AHE has been used to remove the problem of uneven illumination. The proposed technique has the ability to remove the limitations of existing techniques. Different kind of image quality assessment metrics have been used to evaluate the effectiveness of proposed technique over the existing one. The proposed technique give efficient results for different hazy underwater images by removing the haze to a good extent and improving the quality.

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Low Complexity Underwater Image Enhancement Based on Dark Channel Prior

Cited by 147 publications

References 10 publications

An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

An Underwater Image Enhancement Algorithm for Environment Recognition and Robot Navigation

Single underwater image enhancement with a new optical model

Performance Evaluation of Fuzzy based DCP and AHE for Underwater Image Haze Removal

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