Underwater image enhancement framework and its application on an autonomous underwater vehicle platform

Li, Tengyue; Rong, Shenghui; Cao, Xueting; Liu, Yongbin; Chen, Long; He, Bo

doi:10.1117/1.oe.59.8.083102

Cited by 25 publications

(20 citation statements)

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“…To showcase the efficacy of the proposed approach, we carried out comprehensive evaluations on real-world UAV-based degraded backlit images in high-steep slope scenes. We chose several state-of-the-art degraded image restoration techniques for comparison, including the methods of Li et al [ 40 ], Fu et al [ 7 ], Wang et al [ 8 ], Shi et al [ 6 ], Zhang et al [ 41 ], Ying et al [ 36 ], Lv et al [ 27 ], and Wu et al [ 35 ]. Among them, the conventional methods of Li et al [ 40 ], Fu et al [ 7 ], Wang et al [ 8 ], Shi et al [ 6 ], Zhang et al [ 41 ], and Ying et al [ 36 ] mainly focus on removing uneven illumination, separating the illumination layer, or improving the contrast.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…We chose several state-of-the-art degraded image restoration techniques for comparison, including the methods of Li et al [ 40 ], Fu et al [ 7 ], Wang et al [ 8 ], Shi et al [ 6 ], Zhang et al [ 41 ], Ying et al [ 36 ], Lv et al [ 27 ], and Wu et al [ 35 ]. Among them, the conventional methods of Li et al [ 40 ], Fu et al [ 7 ], Wang et al [ 8 ], Shi et al [ 6 ], Zhang et al [ 41 ], and Ying et al [ 36 ] mainly focus on removing uneven illumination, separating the illumination layer, or improving the contrast. The techniques presented by Lv et al [ 27 ] and Wu et al [ 35 ] are representative CNN-based algorithms designed to enhance low-light images through feature map manipulation.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The techniques presented by Lv et al [ 27 ] and Wu et al [ 35 ] are representative CNN-based algorithms designed to enhance low-light images through feature map manipulation. Additionally, the methods introduced by Li et al [ 40 ], Fu et al [ 7 ], Wang et al [ 8 ], Shi et al [ 6 ], Zhang et al [ 41 ], and Ying et al [ 36 ] and the proposed approach were executed on a personal computer running the Windows 10 operating system, equipped with an Intel i7-9700K CPU (Santa Clara, CA, USA) clocked at 3.60 GHz. The source code for the proposed method was implemented using Matlab [ 46 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…These methods can yield excellent results, but they lack a robust physical explanation. Furthermore, in recent years, several underwater image enhancement methods [ 40 , 41 ] have demonstrated potential in restoring low-contrast images. These methods have reported generalizability to land-based low-light images.…”

Section: Related Workmentioning

confidence: 99%

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Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope

2024

Sensors

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Unmanned aerial vehicle (UAV)-based geological mapping is significant for understanding the geological structure in the high-steep slopes, but the images obtained in these areas are inevitably influenced by the backlit effect because of the undulating terrain and the viewpoint change of the camera mounted on the UAV. To handle this concern, a novel backlit image restoration method is proposed that takes the real-world application into account and addresses the color distortion issue existing in backlit images captured in high-steep slope scenes. Specifically, there are two main steps in the proposed method, which consist of the backlit removal and the color and detail enhancement. The backlit removal first eliminates the backlit effect using the Retinex strategy, and then the color and detail enhancement step improves the image color and sharpness. The author designs extensive comparison experiments from multiple angles and applies the proposed method to different engineering applications. The experimental results show that the proposed method has potential compared to other main-stream methods both in qualitative visual effects and universal quantitative evaluation metrics. The backlit images processed by the proposed method are significantly improved by the process of feature key point matching, which is very conducive to the fine construction of 3D geological models of the high-steep slope.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope

2024

Sensors

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“…Illumination correction, also called illumination estimation or color constancy calculation [1][2], eliminates the influence of illumination on color, thereby obtaining the inherent color of the object itself. Although the conventional unsupervised illumination correction algorithm is uncomplicated and liable to achieve, it exhibits low performance when it does not meet the assumptions.…”

Section: Introductionmentioning

confidence: 99%

Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

2023

KSII TIIS

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In a random vector functional link (RVFL) network, shortcomings such as local optimal stagnation and decreased convergence performance cause a reduction in the accuracy of illumination correction by only inputting the weights and biases of hidden neurons. In this study, we proposed an improved regularized random vector functional link (RRVFL) network algorithm with an optimized grey wolf optimizer (GWO). Herein, we first proposed the moth-flame optimization (MFO) algorithm to provide a set of excellent initial populations to improve the convergence rate of GWO. Thereafter, the MFO-GWO algorithm simultaneously optimized the input feature, input weight, hidden node and bias of RRVFL, thereby avoiding local optimal stagnation. Finally, the MFO-GWO-RRVFL algorithm was applied to ameliorate the performance of illumination correction of various test images. The experimental results revealed that the MFO-GWO-RRVFL algorithm was stable, compatible, and exhibited a fast convergence rate.

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A Recent Review of Underwater Image Enhancement Techniques

Ghalib,

Alyasseri

2023

Lecture Notes in Networks and Systems

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Underwater image enhancement framework and its application on an autonomous underwater vehicle platform

Cited by 25 publications

References 0 publications

Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope

Restoration of UAV-Based Backlit Images for Geological Mapping of a High-Steep Slope

Illumination correction via improved grey wolf optimizer for regularized random vector functional link network

A Recent Review of Underwater Image Enhancement Techniques

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