Underwater Image Restoration Using Color-Line Model

Zhou, Yuan; Wu, Qiong; Yan, Kangming; Feng, Liyang; Wang, Xiang

doi:10.1109/tcsvt.2018.2884615

Cited by 82 publications

(29 citation statements)

References 17 publications

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“…In addition, haze-line-based [22] and color-line-based [23] methods were gradually applied to underwater restoration. Moreover, Berman et al [22] constructed a distance maps dataset.…”

Section: A Underwater Image Restoration Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multiscale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods. INDEX TERMS Underwater image enhancement, Pixel intensity center regionalization, Histogram equalization, Multi-scale fusion.

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“…In addition, haze-line-based [22] and color-line-based [23] methods were gradually applied to underwater restoration. Moreover, Berman et al [22] constructed a distance maps dataset.…”

Section: A Underwater Image Restoration Methodsmentioning

confidence: 99%

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

et al. 2020

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“…Since the underwater imaging environment is similar to the foggy environment, thereby the DCP gradually applied to underwater image restoration [48]- [60]. Carlevaris-Bianco et al [48] proposed a difference between the maximum value of the R, G and B channels, which is not the minimum value directly selected in the DCP.…”

Section: Prior Knowledge-based Methodsmentioning

confidence: 99%

A Survey of Restoration and Enhancement for Underwater Images

et al. 2019

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Images captured underwater usually suffer from color distortion, detail blurring, low contrast, and a bluish or greenish tone due to light scattering and absorption in the underwater medium, which in turn the visibility is adversely affected by these factors seriously. Over the last decades, various image restoration and enhancement methods have been developed by many researchers to improve the quality (visibility and highlight richer details) of underwater images. This paper introduces the overview of state-of-the-art underwater image restoration and enhancement techniques and classifies the approaches in two categories: image restoration (physical-based model) and image enhancement (nonphysical-based model). Furthermore, the classification of these two methods is elaborated. Then, the typical underwater image restoration and enhancement methods are discussed in detail, as well as a comprehensive study and fair evaluation of the methods is carried out from both qualitative and quantitative perspectives. Finally, the research process of underwater image restoration and enhancement is summarized and the suggestions for future research are prospected. INDEX TERMSUnderwater image degradation, underwater image restoration, underwater image enhancement, underwater image quality evaluation, transmission map estimation.

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“…(2017) To fill this hole, this paper presents a unique fusion-based technique to misuse shading exchange while tuning the shading amendment locally, as a component of the light lessening dimension assessed from the red channel. The DCP [16] is then used to reestablish the shading repaid picture, by rearranging the disentangled Koschmieder light transmission display, concerning open air dehazing. Our system upgrades picture differentiate in a very compelling way and furthermore underpins precise transmission map estimation.…”

Section: )mentioning

confidence: 99%

A Study based on Underwater Image Restoration: A Survey

Vashishtha¹

2019

IJRASET

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In an underwater (UW) environment, the light always gets spoiled and is absorbed because it travels from the camera to the object, which seriously affects the quality of the image. As a result, unlike the underwater images, they are bad, as if a mist covers them. Due to the physical properties of the UW condition, due to the artist's color & the few of light, the visibility under water is suffering from poor visibility. We estimates that visual brightness removing moderate interference on light exposure. Underwater imaging is an important subject in marine research. Images taken under water often suffer due to the absorption and absorption of color scattering and low contrast light. The underwater image can be prepared in the form of a fair image & a mixture of upbringing light, in which the comparative volume of all camera is deeply fixed. The researches mainly based on improving the quality of image and also focused on reducing the noise.

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Underwater Image Restoration Using Color-Line Model

Cited by 82 publications

References 17 publications

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

A Survey of Restoration and Enhancement for Underwater Images

A Study based on Underwater Image Restoration: A Survey

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