Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing

Liang, Zheng; Wang, Yafei; Ding, Xueyan; Mi, Zetian; Fu, Xianping

doi:10.1016/j.neucom.2020.03.091

Cited by 84 publications

(28 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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. Zhang et al [18] used sub-interval linear transformation and dual-interval histograms to correct colors and enhance contrast, and obtained high-quality underwater images.…”

Section: Enhancement-based Methodsmentioning

confidence: 99%

Attention-based for Multiscale Fusion Underwater Image Enhancement

2022

KSII TIIS

View full text Add to dashboard Cite

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.

show abstract

Section: Enhancement-based Methodsmentioning

confidence: 99%

Attention-based for Multiscale Fusion Underwater Image Enhancement

2022

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…Zhang et al [27] proposed a multi-scale retinex method combining the bilateral and trilateral filters on the three channels of the image in CIELAB color space, according to the characteristics of each channel. More recently, Liang et al proposed an attenuation map-guided approach to underwater image color correction [28]. With an attenuation map of each color channel, a piecewise linear transform is used to correct the colors of each channel.…”

Section: Traditional Color Correction-based Underwater Image Enhancementmentioning

confidence: 99%

Underwater image enhancement with latent consistency learning‐based color transfer

Yang

Tian

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

Due to the inevitable wavelength-dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learningbased approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation-enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of colortransfer techniques, it is found that clear template image-assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color-transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete colorcorrection models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.

show abstract

“…Channel-wise and spatial attention modules are used to explore the inter-dependencies among the channel-wise features and spatial positions of features, respectively. Liang et al [19] used attenuation map and detailed preservation method to recover the smoggy images. A piecewise linear transform is used for each color channel of attenuation map to process the information.…”

Section: Literature Reviewmentioning

confidence: 99%

Image smog restoration using oblique gradient profile prior and energy minimization

Kumar

Jain

2021

Front. Comput. Sci.

View full text Add to dashboard Cite

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics. Electronic Supplementary Material Supplementary material is available in the online version of this article at 10.1007/s11704-020-9305-8.

show abstract

Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing

Cited by 84 publications

References 27 publications

Attention-based for Multiscale Fusion Underwater Image Enhancement

Attention-based for Multiscale Fusion Underwater Image Enhancement

Underwater image enhancement with latent consistency learning‐based color transfer

Image smog restoration using oblique gradient profile prior and energy minimization

Contact Info

Product

Resources

About