Underwater image enhancement based on the dark channel prior and attenuation compensation

Guo, Qingwen; Xue, Lulu; Tang, Ruichun; Guo, Lingrui

doi:10.1007/s11802-017-3242-7

Cited by 18 publications

(4 citation statements)

References 10 publications

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“…Fusion of contrast enhancement and de-hazing. Guo et al 125 presented an improved segmentation dark channel prior method to remove fog and enhance contrast for underwater images. De-hazing occurs because of light refraction, and contrast degradation occurs because attenuation varies with wavelength.…”

Section: Fusion-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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valuable resources that exist beneath Earth's surface. Underwater exploration requires enhanced images that are obtained using enhancement methods. So, it is important that underwater image enhancement (UIE) methods work well in terms of performance and accuracy. As a result, research in UIE has increased in the past few years. An extensive survey is conducted on existing UIE methods along with their broad classification, underwater datasets, and evaluation metrics, respectively. The experimental analysis is conducted to compare the existing UIE methods in terms of qualitative and quantitative evaluation metrics. The real-world applications and future scope of existing enhancement methods are highlighted and discussed.

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Section: Fusion-based Methodsmentioning

confidence: 99%

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma

Kumar

2022

J. Electron. Imag.

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“…A simplified underwater image formation model is used to describe the haze image [ 17 ]:

where I C ( x , y ) represents the image intensity captured by the camera from the scene at the position ( x , y ); J C refers to the corresponding haze-free image; B C denotes the background lights, which is a three-dimensional vector; C ϵ { R , G , B } denotes one of the R–G–B color channels; and t C ( x , y ) denotes the transmission map, and it is an exponential attenuation function related to the spectral volume attenuation coefficient β ( x , y ) and the scene depth d ( x , y ), which can be expressed as follows:

…”

Section: Related Workmentioning

confidence: 99%

Enhancement and Optimization of Underwater Images and Videos Mapping

Xiang

Wang

et al. 2023

Sensors

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Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

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“…The accuracy of underwater target recognition based on visible light imaging depends on the imaging quality, which determines the effectiveness of underwater target feature extraction and directly affects the accuracy of underwater target recognition [2][3] . Due to the fact that the main influencing factor of the visible light imaging quality of underwater targets is the back scatter of water bodies, and in underwater target recognition application scenarios, the imaging environment is often harsh, the water environment is dark, and sediment interference and occlusion all lead to insufficient visible light imaging quality, posing challenges to feature extraction of underwater targets [4][5] .…”

Section: Introducementioning

confidence: 99%

Target recognition method based on image simulation technology of underwater target in harsh environment

Lin,

Wang,

Guo

et al. 2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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In order to study the recognition methods of underwater targets in harsh environments and improve the accuracy of image based underwater target recognition, a deep learning based underwater target recognition method in harsh environments was carried out. Propose a simulation method for underwater target harsh environment images based on clearer image samples. The simulation processing mainly includes adding random scale and spatial position obstructions, adding random noise, and obtaining simulated image data. Establish an integrated image dataset, which includes clearer original image data and simulated image data. The recognition results of target recognition models trained on the integrated image dataset have significantly improved compared to the original image dataset.

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Underwater image enhancement based on the dark channel prior and attenuation compensation

Cited by 18 publications

References 10 publications

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Enhancement and Optimization of Underwater Images and Videos Mapping

Target recognition method based on image simulation technology of underwater target in harsh environment

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