Underwater Image Enhancement Using Scene Depth-Based Adaptive Background Light Estimation and Dark Channel Prior Algorithms

Yang, Shudi; Chen, Zhehan; Feng, Zhipeng; Xiao-ming, MA

doi:10.1109/access.2019.2953463

Cited by 22 publications

(8 citation statements)

References 33 publications

(42 reference statements)

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“…To dealing with the lowlight deep-sea circumstance, Li [25] proposes an adaptive bright-color channel based low-light underwater enhancement method followed by a denoising and a color correction method to enhance such images and remove noise and artifacts. In [10], Yang et al combine maximum scene depth estimation and adaptive color correction to accurately estimate the background light. To taking advantage of both the image-based and model-based approaches, Chang et al [26] compute two transmission maps from distinct perspectives and fuse them weighted by their saliency maps.…”

Section: Underwater Image Restoration Methodsmentioning

confidence: 99%

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Multi-Purpose Oriented Real-World Underwater Image Enhancement

2020

IEEE Access

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Images captured underwater usually suffer from weak illumination, color cast, fuzz and noise, which severely degrade the visibility. Numerous methods have been proposed to improve the quality of underwater images, but rarely of them can give a comprehensive consideration to all these problems, which makes them hard to adapt for various and complex real-world underwater scenes. Herein, a novel multipurpose oriented approach for real-world underwater image enhancement is proposed. To manipulate different information on the corresponding layers, we firstly decompose the input image into illumination layer and reflectance layer. Subsequently, compensation of the brightness is carried out on the illumination layer, while color correction and contrast enhancement are implemented on the reflectance layer through a multi-scale processing strategy. Benefiting from this strategy, the proposed approach is provided with high control flexibility, which can significantly improve the visibility of underwater images while efficiently suppress the amplification of noise. Both qualitative and quantitative evaluations demonstrate that the proposed method has superior robustness, accuracy and effectiveness for complex marine circumstance.

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Section: Underwater Image Restoration Methodsmentioning

confidence: 99%

“…However, since the attenuation of light under water is wavelength-dependent and less homogeneous than in outdoor, DCP-based methods show severe limitations. Although physical characteristics of water are incorporated in the improved DCP [8] [9] [10], it still hard to adapt for various water types.…”

Section: Introductionmentioning

confidence: 99%

Multi-Purpose Oriented Real-World Underwater Image Enhancement

2020

IEEE Access

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“…38 People use digital image processing technology or DL methods to improve the visual quality of underwater images. For the former, mathematical methods, such as histogram transform, 39,40 physical model, 41 visual prior, 42 and Retinex 43 are used to enhance image contrast or restore color degradation.…”

Section: Underwater Image Enhancementmentioning

confidence: 99%

UStark: underwater image domain-adaptive tracker based on Stark

Huo

Liu

et al. 2022

J. Electron. Imag.

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. Though the open-air tracker has achieved an advanced level, its design is still a challenging task for degraded underwater images. Using underwater enhancement technology can improve the performance of underwater trackers. However, most underwater image enhancement methods focus on improving the visual effect rather than serving the tracker better. Therefore, we intended to explore a simple but powerful image domain-adaptive method to improve Stark’s performance by enhancing Stark’s input images. Specifically, it consists of underwater image adaptation network (UIAN) with double heads and adaptation block based on scene estimation (ABSE) that consists of three independent image processing modules without deep learning. UIAN is used to predict the category of image domain and parameters of ABSE. ABSE decodes the parameters and sequentially process underwater images in each module. The training of UIAN is independent of the training of the tracker. After training the class prediction head of UIAN first, freezing its weights, by initializing and tracking in one enhanced image and computing tracker’s loss, the parameter head can be trained to make sure UIANs hyperparameters can match the Stark tracker. The UStark proposed can adaptively process clear and degraded underwater images. Compared with Stark, UStark has improved the accuracy and success rate in typical underwater environments by 3.7% and 1.5% (blue), 5% and 3.4% (yellow), and 5.4% and 3.3% (dark), respectively. In addition, compared with other underwater image enhancement methods, our method can enhance the performance of the tracker in more categories of underwater images.

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“…The software based approaches helps in recovering underwater images by utilising efficient algorithms. These software approaches can be further divided into image restoration method, colour correction methods, dark channel prior (DCP) based methods, fusion based methods and convolutional neural networks (CNNs) based methods [13][14][15][16][17][18][19][20][21][22][23].…”

Section: Literature Surveymentioning

confidence: 99%

“…Background light estimation algorithm is proposed by Yang et al. [14] to enhance the underwater images between 30–60 m depth with the help of artificial light. This method basically combines DCP and deep learning for obtaining red channel information of the background light, which is further improved by adaptive colour deviation.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancement algorithm for high visibility of underwater images

Mathur

Goel

2021

IET Image Processing

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Underwater image enhancement has been attracting researchers in present day scenario for exploring the marine life. But underwater images suffer from various glitches like haziness, low contrast and faded colours due to absorption and scattering properties of light in water. To overcome these issues, the present paper proposes an enhancement method for underwater images. The proposed enhancement method comprises of three steps, i.e. automatic white balancing, dehazing and Rayleigh stretching in spatial domain. Automatic white balancing technique removes the colour cast from underwater images. Haze removal algorithm efficiently overcomes the haziness but it also reduces the local contrast and making the image appear dull. For improving the dehazing result and the visual quality simultaneously, the proposed method process the image by histogram stretching technique based on Rayleigh distribution. The proposed method corrects the colour cast and improves the contrast along with removing the haze from underwater images. Subjective and objective analysis on U 45 dataset validates the efficiency of proposed method and the enhanced images achieve better visual quality as compared to the existing methods. High values of EMEE, EME, UIQM and UCIQE for different types of underwater images further proves the potential and efficacy of the proposed method. INTRODUCTIONAn unexplored huge world present under oceans is attracting various researcher and scientists to study and investigate variety of marine life forms, incredible landscape, mysterious shipwrecks and beautiful coral reefs. Underwater environment is also gaining substantial attention due to increase in optical robotic applications and marine engineering [1,2]. But complex underwater environment and poor lighting conditions are major challenges to capture good quality underwater images. Water is a denser medium than the air and sunlight is reflected and refracted partially as it enters the ocean due to transition from one medium to the other. Less light will be reflected when ocean water is calm and smooth rather than when it is turbulent. The part of light which penetrates the ocean surface is affected by the phenomenon of refraction because light travels faster in air as compared to water. These refracted rays of light further interact with the water molecules and suspended solid particles leading to scattering and absorption. Scattering reduces intensity of light causing loss in contrast and haziness in underwater images. Whereas, absorption of This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Underwater Image Enhancement Using Scene Depth-Based Adaptive Background Light Estimation and Dark Channel Prior Algorithms

Cited by 22 publications

References 33 publications

Multi-Purpose Oriented Real-World Underwater Image Enhancement

Multi-Purpose Oriented Real-World Underwater Image Enhancement

UStark: underwater image domain-adaptive tracker based on Stark

Enhancement algorithm for high visibility of underwater images

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