Underwater image enhancement using guided trigonometric bilateral filter and fast automatic color correction

Lu, Huimin; Li, Yujie; Serikawa, Seiichi

doi:10.1109/icip.2013.6738704

Cited by 87 publications

(39 citation statements)

References 17 publications

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“…Visually-guided AUVs (Autonomous Underwater Vehicles) and ROVs (Remotely Operated Vehicles) are widely used in important applications such as the monitoring of marine species migration and coral reefs [39], inspection of submarine cables and wreckage [5], underwater scene analysis, seabed mapping, human-robot collaboration [24], and more. One major operational challenge for these underwater robots is that despite using high-end cameras, visual sensing is often greatly affected by poor visibility, light refraction, absorption, and scattering [31,45,24]. These optical artifacts trigger non-linear distortions in the captured images, which severely affect the performance of visionbased tasks such as tracking, detection and classification, Input Generated (a) Perceptual enhancement of underwater images.…”

Section: Introductionmentioning

confidence: 99%

Fast Underwater Image Enhancement for Improved Visual Perception

Islam

Xia

Sattar

2020

IEEE Robot. Autom. Lett.

840

414

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In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a largescale dataset of a paired and an unpaired collection of underwater images (of 'poor' and 'good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at https://github.com/xahidbuffon/ funie-gan.

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Section: Introductionmentioning

confidence: 99%

Fast Underwater Image Enhancement for Improved Visual Perception

Islam

Xia

Sattar

2020

IEEE Robot. Autom. Lett.

840

414

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“…Nrer is the normalized residual energy ratio [14]. For improving the image quality, we take the processing flowchart as Fig.1.…”

Section: Shallow Underwater Imaging Modelmentioning

confidence: 99%

Underwater image enhancement using inherent optical properties

et al. 2015

2015 IEEE International Conference on Information and Automation

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This paper describes a novel method of enhancing shallow ocean optical images or videos using a fast dark channel prior descattering method. Absorption, scattering and color distortion are three major distortion problems for underwater imaging. In this paper, we propose a novel shallow water imaging model to compensate for the attenuation discrepancy along the propagation path and an effective underwater scene enhancement scheme. The recovered images are characterized by a reduced noised level, better exposure of the dark regions, and improved global contrast where the finest details and edges are enhanced significantly.

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“…However, this algorithm requires significant computation time with a complexity of O(N 2 ), and the processed images may have artificial halos in some cases. To overcome this disadvantage, He et al also proposed a guided image filter [12], which used the foggy image as a reference image. However, this method leads to incomplete haze removal and does not meet the requirements for realtime processing.…”

Section: Introductionmentioning

confidence: 99%

Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

Nakashima

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYAbsorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade underwater optical images. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. In the present paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a fast weighted guided normalized convolution domain filtering algorithm for enhancing underwater optical images. The enhanced images are characterized by a reduced noise level, better exposure in dark regions, and improved global contrast, by which the finest details and edges are enhanced significantly.

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Underwater image enhancement using guided trigonometric bilateral filter and fast automatic color correction

Cited by 87 publications

References 17 publications

Fast Underwater Image Enhancement for Improved Visual Perception

Fast Underwater Image Enhancement for Improved Visual Perception

Underwater image enhancement using inherent optical properties

Turbidity Underwater Image Restoration Using Spectral Properties and Light Compensation

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