RUIG: Realistic Underwater Image Generation Towards Restoration

Tabib, Ramesh Ashok; Reddy, Sai Sudheer; Patil, Ujwala; Mudenagudi, Uma

doi:10.1109/cvprw53098.2021.00247

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…Jamadandi and Mudenagudi [29] proposed a new deep learning framework combined with wavelet transform, which turned the underwater image enhancement problem into a realistic style conversion problem. Desai et al [30] synthesized underwater style images based on the revised underwater imaging model [6], and trained on the proposed conditional generation confrontation network.…”

Section: Deep Learning-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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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Attention-based for Multiscale Fusion Underwater Image Enhancement

2022

KSII TIIS

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“…Underwater image enhancement based on generative adversarial networks is also a research hotspot in recent years. For example, by simulating underwater imaging degradation (absorption and scattering), C et al [17] proposed a novel method for generating synthetic underwater images considering revised image formation model and use the generated synthetic underwater images to train a CGAN towards restoration of degraded underwater images. Yang et al [18] also develop a generative adversarial network-based method for underwater image enhancement.…”

Section: Related Theoriesmentioning

confidence: 99%

“…For example, by simulating underwater imaging degradation (absorption and scattering), C et al. [17] proposed a novel method for generating synthetic underwater images considering revised image formation model and use the generated synthetic underwater images to train a CGAN towards restoration of degraded underwater images. Yang et al.…”

Section: Related Theoriesmentioning

confidence: 99%

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Shi

Wang

Zhou

et al. 2022

IET Image Processing

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Underwater images usually suffer from colour distortion, blur, and low contrast, which hinder the subsequent processing of underwater information. To address these problems, this paper proposes a novel approach for single underwater images enhancement by integrating data‐driven deep learning and hand‐crafted image enhancement techniques. First, a statistical analysis is made on the average deviation of each channel of input underwater images to that of its corresponding ground truths, and it is found that both the red channel and the green channel of an underwater image contribute to its colour distortion. Concretely, the red channel of an underwater image is usually seriously attenuated, and the green channel is usually over strengthened. Motivated by such an observation, an attention mechanism guided residual module for underwater image colour correction is proposed, where the colour of the red channel of the underwater image and that of the green channel is compensated in a different way, respectively. Coupled with an attention mechanism, the residual module can adaptively extract and integrate the most discriminative features for colour correction. For scene contrast enhancement and scene deblurring, the traditional image enhancement techniques such as CLAHE (contrast limited adaptive histogram equalization) and Gamma correction are coupled with a multi‐scale convolutional neural network (MSCNN), where CLAHE and Gamma correction are used as complement to deal with the complex and changeable underwater imaging environment. Experiments on synthetic and real underwater images demonstrate that the proposed method performs favourably against the state‐of‐the‐art underwater image enhancement methods.

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“…Deep learning has proven to perform well in various underwater tasks, but it is difficult to obtain large datasets in deep-sea environments. How to generate underwater images from existing resources is an important and challenging task [29,44,18,3,45,10]. In [3,10], authors constructed a large number of synthetic datasets utilizing previously calculated ocean attenuation coefficients combined with underwater attenuation models.…”

Section: Related Workmentioning

confidence: 99%

“…How to generate underwater images from existing resources is an important and challenging task [29,44,18,3,45,10]. In [3,10], authors constructed a large number of synthetic datasets utilizing previously calculated ocean attenuation coefficients combined with underwater attenuation models. Hou et al [18] developed the quadtree to select the background light area and obtained the transmission map according to the DCP principle [16].…”

Section: Related Workmentioning

confidence: 99%

Underwater Light Field Retention : Neural Rendering for Underwater Imaging

Tian¹,

Chen²,

Liu³

et al. 2022

Preprint

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Underwater Image Rendering aims to generate a true-tolife underwater image from a given clean one, which could be applied to various practical applications such as underwater image enhancement, camera filter, and virtual gaming. We explore two less-touched but challenging problems in underwater image rendering, namely, i) how to render diverse underwater scenes by a single neural network? ii) how to adaptively learn the underwater light fields from natural exemplars, i,e., realistic underwater images? To this end, we propose a neural rendering method for underwater imaging, dubbed UWNR (Underwater Neural Rendering). Specifically, UWNR is a data-driven neural network that implicitly learns the natural degenerated model from authentic underwater images, avoiding introducing erroneous biases by hand-craft imaging models.Compared with existing underwater image generation methods, UWNR utilizes the natural light field to simulate the main characteristics of the underwater scene. Thus, it is able to synthesize a wide variety of underwater images from one clean image with various realistic underwater images.Extensive experiments demonstrate that our approach achieves better visual effects and quantitative metrics over previous methods. Moreover, we adopt UWNR to build an open Large Neural Rendering Underwater Dataset containing various types of water quality, dubbed LNRUD.

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RUIG: Realistic Underwater Image Generation Towards Restoration

Cited by 19 publications

References 23 publications

Attention-based for Multiscale Fusion Underwater Image Enhancement

Attention-based for Multiscale Fusion Underwater Image Enhancement

Integrating deep learning and traditional image enhancement techniques for underwater image enhancement

Underwater Light Field Retention : Neural Rendering for Underwater Imaging

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