Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Verma, Gunjan; Kumar, Mahendra

doi:10.1117/1.jei.31.6.060901

Cited by 9 publications

(6 citation statements)

References 130 publications

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“…Zhang et al 6 proposed an extended multi-scale Retinex-based UIE method, which processed the underwater images in CIELAB color space. 7 Liu and Chau 8 observed that the dark channel of underwater images tended to zero and designed a UIE method based on contrast enhancement. Peng et al 9 proposed an underwater scene depth estimation method called IBLA based on image blurring and light absorption.…”

Section: Traditional Methodsmentioning

confidence: 99%

UKD-Net: efficient image enhancement with knowledge distillation

Zhao,

Cai,

Xue

et al. 2024

J. Electron. Imag.

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Underwater images often suffer from color distortion, blurred details, and low contrast. Therefore, more researchers are exploring underwater image enhancement (UIE) methods. However, UIE models based on deep learning suffer from high computational complexity, thus limiting their integration into underwater devices. In this work, we propose a lightweight UIE network based on knowledge distillation (UKD-Net), which includes a teacher network (T-Net) and a student network (S-Net). T-Net uses our designed multi-scale fusion block and parallel attention block to achieve excellent performance. We utilize knowledge distillation technology to transfer the rich knowledge of the T-Net onto a deployable S-Net. Additionally, S-Net employs blueprint separable convolutions and multistage distillation block to reduce parameter count and computational complexity. Results demonstrate that our UKD-Net successfully achieves a lightweight model design while maintaining superior enhanced performance.

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Section: Traditional Methodsmentioning

confidence: 99%

UKD-Net: efficient image enhancement with knowledge distillation

Zhao,

Cai,

Xue

et al. 2024

J. Electron. Imag.

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“…In order to prove the de-fogging effect of the improved dark channel de-fogging algorithm in this paper, the de-fogging effect was compared with that of He Keming, Fattle and Tan respectively. Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM [9,10] ) were used to measure the image quality after de-fogging. The peak signal-to-noise ratio (PSNR) is used to express the ratio between the maximum possible power of a signal and the power of destructive noise that affects the fidelity of its representation.…”

Section: Quantitative Analysismentioning

confidence: 99%

Research on image de-fogging algorithm for rocket recovery

LU,

SUN,

CAO

et al. 2023

Third International Computing Imaging Conference (CITA 2023)

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In the rocket recovery environment, the imaging conditions are complicated, and the vision equipment is prone to problems such as ground target information loss, image contrast and saturation reduction, color deviation and so on, which directly affect the solution of the rocket pose. Therefore, it is necessary to study the de-fog algorithm for the special environment of rocket recovery, so as to contribute to the solution of rocket pose and altitude measurement. Through the investigation of previous research on de-fogging algorithms, it can be found that the dark channel de-fogging algorithm is not only simple in principle but also has better de-fogging effect in changeable environment. In addition, the brightness of fog in rocket recovery is often affected by the flame emitted from the nozzle, and a single dark channel de-fogging algorithm cannot adjust the overall brightness according to the environmental characteristics, resulting in blurred feature points in the image after de-fogging. Therefore, considering that RETINEX theory can be used to adjust the brightness of images, in combination with the dark channel prior model, this paper mainly uses RETINEX theory to improve the dark channel de-fogging theory to process the transmission t(x), so that the final image retains the feature information to the maximum extent, and improves the adaptability of the dark channel de-fogging algorithm to different fog environments. Finally, the removal effect of different de-fogging algorithms on different concentrations of fog in rocket recovery environment is quantitatively analyzed. The experimental results show that the de-fogging algorithm in this paper can solve the impact of the fog generated by the rocket nozzle on the vision equipment.

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“…According to the "center-periphery difference", the difference between the images of different layers of the pyramid is calculated to obtain the brightness, color and direction feature images. Before calculating the difference value, the method of reducing the difference value of the image is adopted to unify the size of the image [21]. The calculation method is as follows.…”

Section: Visual Saliency Analysismentioning

confidence: 99%

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

Zhang,

Bi,

2023

Preprint

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The underwater image obtained is difficult to satisfy human visual perception because of the particle scattering and water absorption phenomena when visible light propagates underwater. In underwater images, light absorption easily leads to image distortion and reduction of image contrast and brightness. Therefore, this work aims to improve the quality of underwater image processing, reduce the distortion rate of underwater images, and further improve the efficiency of underwater image extraction, processing, and tracking. This work combines intelligent blockchain technology in emerging multimedia industries with existing image processing technology to improve the target detection capability of image processing algorithms. Firstly, the theory of visual saliency analysis (VSA) is studied. The steps of image processing using VSA are analyzed. Based on the original Itti model, the visual significance detection step is optimized. Then, the theoretical basis and operation steps of particle swarm optimization (PSO) algorithm in intelligent blockchain technology are studied. VSA theory is combined with PSO to design underwater image processing algorithms and target detection optimization algorithms for underwater images. The experimental results show that: (1) the method has a higher F value and lower Mean Absolute Error. (2) Compared with the original image, the restored image entropy through this method is greatly improved, and the information in the image increases. Therefore, this method has good performance. Besides, this method performs well in image definition, color, and brightness. The quality of the restored image through this method is better than that of other algorithms. (3) Compared with similar algorithms, the relative errors of this method are reduced by 2.56%, 3.24% and 3.89%, respectively. The results show that the method has high accuracy. The research results can provide a reference for future underwater image processing and target detection research. In addition, the designed underwater image processing and target detection and tracking algorithms can improve the detection efficiency and accuracy of underwater targets and help to accurately obtain underwater target images.

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Systematic review and analysis on underwater image enhancement methods, datasets, and evaluation metrics

Cited by 9 publications

References 130 publications

UKD-Net: efficient image enhancement with knowledge distillation

UKD-Net: efficient image enhancement with knowledge distillation

Research on image de-fogging algorithm for rocket recovery

Underwater Image Processing and Target Detection from Particle Swarm Optimization Algorithm

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