Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map

Song, Wei; Wang, Yan; Huang, Dongmei; Liotta, Antonio; Perra, Cristian

doi:10.1109/tbc.2019.2960942

Cited by 191 publications

(99 citation statements)

References 47 publications

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“…In order to evaluate the performance of the proposed method on the dehazing task, qualitative and quantitative comparison are carried out, respectively. The methods used for comparisons include MIP method [18], Underwater Dark Channel Prior (UDCP) method [14], Blue-Green Channels Dehazing and Red Channel Correction (BGCD&RCC) method [16], Image Blurriness and Light Absorption method (IBLA) [17], Underwater Light Attenuation Prior (ULAP) method [21], Statistical Model of BL and Optimization (SMBLO) method [22], Dehaze-Net method [23], Multi-scale Dehazing Convolutional Neural Network (MDCNN) method [38], Minimum Information Loss (MIL) method [19], and Underwater Haze-line (UWHL) method [20]. These selected methods for comparison not only include traditional classical underwater dehazing methods based on the IFM (i.e., UDCP, MIP, and MIP), but also include methods which are proposed in recent years, based on deep learning, statistical models or new optical priors (i.e., IBLA, ULAP, SMBLO, and Dehaze-Net).…”

Section: Resultsmentioning

confidence: 99%

“…Song et al [21] employed the underwater light attenuation prior (ULAP) for underwater image transmission estimation and trained a model with learning-based supervised linear regression. Based on ULAP, they proposed a statistical-based background light estimation models (MABLs) and a new underwater dark channel prior (NUDCP) for estimating a more detailed transmission map [22]. Pan et al [23] developed a CNN, namely Dehaze-Net, to estimate the transmission map and transformed the image into the Hybrid Wavelets and Directional Filter Banks (HWD) domain for de-noising and edge enhancing.…”

Section: Related Workmentioning

confidence: 99%

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Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

et al. 2020

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Underwater images suffer from low visibility and contrast caused by absorption and scattering, which leads to haze and some further limitations. The existing underwater single image dehazing methods cannot achieve a balance between the performance and computational complexity, and are difficult to produce satisfactory results in the regions with large distance. To overcome these problems, we propose a new underwater single image dehazing method, which includes an improved background light estimation based on the quad-tree subdivision iteration algorithm, and a novel transmission estimation method. For the background light estimation, we introduce a robust score for each region of the image, which can evaluate the region from both smoothness and color. For the transmission estimation, we propose the color space dimensionality reduction prior (CSDRP), which allows conversing an image from the three-dimensional RGB color space to a 2D color space, namely the UV color space. In the UV color space, by clustering the pixels into mounts of haze-lines and carefully setting the haze-free boundary, the transmission map can be figured out and used to produce an excellent dehazed image. Experimental results show that our method has competitive effects compared with mainstream underwater single image dehazing methods. INDEX TERMS Underwater image dehazing, contrast enhancement, image enhancement, scattering removal.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

et al. 2020

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“…The color correction curve is estimated through the CIE L*a*b* color space. Song et al [16] established the first database for underwater image background light estimation, and divided the underwater image enhancement process into two steps, first image restoration by the proposed background light estimation model and the optimal transmission map optimizer, and then color correction by an improved white balance algorithm. Considering the wavelength-dependent attenuation of different colors, Liang et al [17] proposed to solve the color distortion problem with the attenuation map of each color channel, and then based on multi-scale decomposition to eliminate the fogging effect and compensate for the loss of details.…”

Section: Enhancement-based Methodsmentioning

confidence: 99%

Attention-based for Multiscale Fusion Underwater Image Enhancement

2022

KSII TIIS

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Underwater images often suffer from color distortion, blurring and low contrast, which is caused by the propagation of light in the underwater environment being affected by the two processes: absorption and scattering. To cope with the poor quality of underwater images, this paper proposes a multiscale fusion underwater image enhancement method based on channel attention mechanism and local binary pattern (LBP). The network consists of three modules: feature aggregation, image reconstruction and LBP enhancement. The feature aggregation module aggregates feature information at different scales of the image, and the image reconstruction module restores the output features to high-quality underwater images. The network also introduces channel attention mechanism to make the network pay more attention to the channels containing important information. The detail information is protected by real-time superposition with feature information. Experimental results demonstrate that the method in this paper produces results with correct colors and complete details, and outperforms existing methods in quantitative metrics.

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“…Peng et al [31] presented a Generalized Dark Channel Prior (GDCP) method by integrating an adaptive color correction algorithm. Song et al [32] estimated the transmission map of the red channel by a new Underwater Dark Channel Prior (NUDCP). Apart from DCP, another line of prior-based algorithms is to apply the optical properties of underwater imaging.…”

Section: A Underwater Image Enhancementmentioning

confidence: 99%

Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement

Fu¹,

Fu²,

Huang³

et al. 2021

Preprint

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To improve the quality of underwater images, various kinds of underwater image enhancement (UIE) operators have been proposed during the past few years. However, the lack of effective objective evaluation methods limits the further development of UIE techniques. In this paper, we propose a novel rank learning guided no-reference quality assessment method for UIE. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image with its low-quality version. Typical mixup algorithms linearly interpolate a given pair of input data. However, the human visual system is non-uniformity and nonlinear in processing images. Therefore, instead of directly training a deep neural network based on the mixed images and their absolute scores calculated by linear combinations, we propose to train a Siamese Network to learn their quality rankings. Twice Mixing is trained based on an elaborately formulated self-supervision mechanism. Specifically, before each iteration, we randomly generate two mixing ratios which will be employed for both generating virtual images and guiding the network training. In the test phase, a single branch of the network is extracted to predict the quality rankings of different UIE outputs. We conduct extensive experiments on both synthetic and real-world datasets. Experimental results demonstrate that our approach outperforms the previous methods significantly.

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Enhancement of Underwater Images With Statistical Model of Background Light and Optimization of Transmission Map

Cited by 191 publications

References 47 publications

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

Attention-based for Multiscale Fusion Underwater Image Enhancement

Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement

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