Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization

Zhou, Jingchun; Pang, Lei; Zhang, Dehuan; Zhang, Weishi

doi:10.1109/joe.2022.3223733

Cited by 118 publications

(48 citation statements)

References 51 publications

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“…Van De Weijer et al [3] proposed a new hypothesis for color constancy, namely the gray-edge theory, which assumes that the average edge difference in a scene is achromatic. Zhou et al [12] proposed a multi-interval subhistogram equalization method, which performs histogram equalization in each subhistogram to improve the image contrast.…”

Section: Traditional Methods Nonphysical Model-based Methodsmentioning

confidence: 99%

Development and validation of BVB-UIE: an underwater image-enhancement model with two-layer deep neural network

Yao,

He,

Bai

et al. 2023

Meas. Sci. Technol.

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Due to the absorption and scattering of light in water, underwater visual visibility is poor, and underwater images usually suffer from color distortion and feature loss, which poses a great challenge for underwater vision tasks. To handle these issues, we present an underwater image enhancement (UIE) method. A Gaussian pyramid is constructed for the degraded underwater images based on an improved visual saliency model, with the characteristics of luminance, orientation, texture, and color. By combining channel and spatial attention mechanisms, a deep asymmetric feature enhancement network is built to preserve the high-dimensional features of the image. In addition, a polynomial loss function is used for the depth hopping supervising constraints during the enhancement process, and the gating signals are employed to control the importance of the features at different spatial locations. These innovations maximize the efficiency of the feature extraction while simplifying the network complexity. Experiments on an open benchmark dataset for UIE show that our method effectively eliminates the color bias and contrast distortion problems while preserving the complex image details compared to the existing UIE algorithms. Objective metrics show a significant improvement in the algorithm, with a 15% increment in the peak signal-to-noise ratio metric compared to the closest competing algorithm.

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Section: Traditional Methods Nonphysical Model-based Methodsmentioning

confidence: 99%

Development and validation of BVB-UIE: an underwater image-enhancement model with two-layer deep neural network

Yao,

He,

Bai

et al. 2023

Meas. Sci. Technol.

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show abstract

“…The autonomous and high-precision detection of marine organisms urgently needs to be realized. However, compared to land-based detection scenes [ 6 , 7 , 8 ], due to the harsh natural conditions of the seabed when collecting images, most of the images in existing underwater datasets are affected by color casts [ 9 , 10 , 11 , 12 , 13 ], low contrast [ 14 , 15 , 16 , 17 ], fuzziness [ 18 , 19 ], noise [ 20 , 21 ], and other quality problems, leading to the loss of clear contour features and partial texture information in the image, which may greatly reduce the usefulness of such images for target recognition. As such, the accuracy of target recognition will be greatly reduced when using such images for target recognition.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5-Based Underwater Object-Detection Framework

Jian

Zhang

Zhou

et al. 2023

Sensors

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To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.

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“…Recently, underwater image enhancement [1][2][3][4][5] for image processing and underwater robotic vision has recently attracted much academic attention. Underwater image enhancement aims to restore the images degraded by wavelength-and distancedependent attenuation and scattering in the underwater environment.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, deep learning technology has led the direction of underwater image enhancement with its powerful capability of feature representation [5]. Most deep learning-based methods directly consider underwater image enhancement as a task of restoration.…”

Section: Introductionmentioning

confidence: 99%

Underwater image enhancement via a channel‐wise transmission estimation network

Wang

Fan

2023

IET Image Processing

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Underwater image enhancement for image processing and underwater robotic vision have recently attracted much academic attention. However, in most existing methods, underwater image enhancement is completed with a simple assumption: the attenuation coefficients are unified across the color channels. This assumption leads to unstable and visually unpleasing enhancement results. Moreover, these methods cannot be successfully applied to explore relatively independent transmissions from multiple color channels with complimentary feature information. To address these challenges, a novel channel‐wise transmission estimation network (CTEN) is proposed, which aims to pioneer the exploration of the transmission difference across the color channels in an underwater scene. Specifically, a color‐specific correction module is proposed to automatically quantify the transmission ability of multiple color channels in the underwater environment. Furthermore, a channel‐wise transmission estimation module is designed to simultaneously explore the relative independence of multi‐color channels and estimate the medium transmissions for each color channel, which represents the attenuation degree of different color radiances after reflecting in the water. Then, a novel residual strategy is introduced to integrate these two modules to complete the underwater enhancement. Using the model, the authors are able to provide an answer as to why channel‐wise transmission estimation are better than single transmission estimation and establish a generalization theory to show the effect of the independent transmission estimation model for each color channel. Experiments on several underwater image datasets verify the superiority of the proposed CTEN model.

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Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization

Cited by 118 publications

References 51 publications

Development and validation of BVB-UIE: an underwater image-enhancement model with two-layer deep neural network

Development and validation of BVB-UIE: an underwater image-enhancement model with two-layer deep neural network

An Improved YOLOv5-Based Underwater Object-Detection Framework

Underwater image enhancement via a channel‐wise transmission estimation network

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