Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

Li, Chongyi; Anwar, Saeed; Hou, Junhui; Cong, Runmin; Guo, Chunle; Ren, Wenqi

doi:10.1109/tip.2021.3076367

Cited by 418 publications

(280 citation statements)

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“…This approach can potentially lead to unreliable outcomes since no independent criterion seems to exist that allows an objective comparison. Recently, many contributions related to UW image colour correction with the use of AI methods [56][57][58][59] try to address this issue by utilising large UW databases, along with their chromatically corrected counterparts. Such a database is the one provided by Li et al [60].…”

Section: Discussionmentioning

confidence: 99%

An Extensive Literature Review on Underwater Image Colour Correction

Vlachos

Skarlatos

2021

Sensors

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The topic of underwater (UW) image colour correction and restoration has gained significant scientific interest in the last couple of decades. There are a vast number of disciplines, from marine biology to archaeology, that can and need to utilise the true information of the UW environment. Based on that, a significant number of scientists have contributed to the topic of UW image colour correction and restoration. In this paper, we try to make an unbiased and extensive review of some of the most significant contributions from the last 15 years. After considering the optical properties of water, as well as light propagation and haze that is caused by it, the focus is on the different methods that exist in the literature. The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.

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Section: Discussionmentioning

confidence: 99%

An Extensive Literature Review on Underwater Image Colour Correction

Vlachos

Skarlatos

2021

Sensors

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“…In The traditional approaches are the dark channel prior (DCP) method [52], the gamma correction (GC) method, the UCM method [25], the Retinex-based method [26], the histogram prior-based method (HP) [20], LAB-MSR [27], the two-step method [16] and the hybrid method [17]. The deep learningbased competitors include two recent GAN-based approaches (MLFcGAN [46] and FUnIE-GAN [47]) and four deep models trained with paired training samples (the U-Net-based enhancement model [53], WaterNet [7], Ucolor [9] and UICoE-Net [8]).…”

Section: Comparison Methodsmentioning

confidence: 99%

“…More recently, Qi et al [8] proposed a two-branch encoder-decoder co-enhancement network. Li et al [9] designed a Ucolor network with multi-color space encoder and a medium transmission-guided decoder.…”

Section: Encoder-decoder Structure-based Networkmentioning

confidence: 99%

“…In an underwater image enhancement dataset that provides both raw underwater images and corresponding clear references, such as the Underwater Image Enhancement Benchmark Enhancements with three latest deep enhancement models, that is, WaterNet [7], UICoE-Net [8], and Ucolor [9], which directly learn to model the mappings between degradation and enhancement. (II) Enhancement with the proposed latent consistency learning-based color transfer (UIEB) [7] dataset, we find that clear underwater images can provided a wealth of reference and constraint information for the color correction and contrast improvement of other raw underwater images.…”

Section: Introductionmentioning

confidence: 99%

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Underwater image enhancement with latent consistency learning‐based color transfer

Yang

Tian

et al. 2022

IET Image Processing

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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.

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“…Researchers have begun to incorporate AI-assisted analysis to underwater imaging improvement models as well. To ameliorate the color shift and increase the contrast of underwater images, Li et al [18] presented a multi-media transmission guide that used an underwater image enhancement network embedded in color space. Guo et al [19] built a multi-scale dense generation confrontation network to solve underwater image color distortion, underexposure, and blur.…”

Section: Introductionmentioning

confidence: 99%

Underwater Diver Image Enhancement via Dual-Guided Filtering

Zhou¹,

Shi²,

Zhang³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The scattering and absorption of light propagating underwater cause the underwater images to present low contrast, color deviation, and loss of details, which in turn make human posture recognition challenging. To address these issues, this study introduced the dual-guided filtering technique and developed an underwater diver image improvement method. First, the color distortion of the underwater diver image was solved using white balance technology to obtain a color-corrected image. Second, dual-guided filtering was applied to the white balanced image to correct the distorted color and enhance its details. Four feature weight maps of the two images were then calculated, and two normalized weight maps were constructed for multi-scale fusion using normalization. To better preserve the obtained image details, the fusion image was histogram-stretched to obtain the final enhanced result. The experimental results validated that this method has improved the accuracy of underwater human posture recognition.

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Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

Cited by 418 publications

References 50 publications

An Extensive Literature Review on Underwater Image Colour Correction

An Extensive Literature Review on Underwater Image Colour Correction

Underwater image enhancement with latent consistency learning‐based color transfer

Underwater Diver Image Enhancement via Dual-Guided Filtering

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