A referenceless image degradation perception method based on the underwater imaging model

Luo, Zhihang; Tang, Zhijie; Jiang, Lizhou; Ma, Gaoqian

doi:10.1007/s10489-021-02815-3

Cited by 9 publications

(3 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The imaging of underwater images in the camera can be divided into direct component, backscattering component, and forward-scattering component [21].…”

Section: Underwater Image Imaging Modelmentioning

confidence: 99%

Underwater Image Enhancement Based on Color Correction and Detail Enhancement

Song

et al. 2022

JMSE

View full text Add to dashboard Cite

To solve the problems of underwater image color deviation, low contrast, and blurred details, an algorithm based on color correction and detail enhancement is proposed. First, the improved nonlocal means denoising algorithm is used to denoise the underwater image. The combination of Gaussian weighted spatial distance and Gaussian weighted Euclidean distance is used as the index of nonlocal means denoising algorithm to measure the similarity of structural blocks. The improved algorithm can retain more edge features and texture information while maintaining noise reduction ability. Then, the improved U-Net is used for color correction. Introducing residual structure and attention mechanism into U-Net can effectively enhance feature extraction ability and prevent network degradation. Finally, a sharpening algorithm based on maximum a posteriori is proposed to enhance the image after color correction, which can increase the detailed information of the image without expanding the noise. The experimental results show that the proposed algorithm has a remarkable effect on underwater image enhancement.

show abstract

“…The imaging of underwater images in the camera can be divided into direct component, backscattering component, and forward-scattering component [21].…”

Section: Underwater Image Imaging Modelmentioning

confidence: 99%

Underwater Image Enhancement Based on Color Correction and Detail Enhancement

Song

et al. 2022

JMSE

View full text Add to dashboard Cite

show abstract

“…IQA technology includes the objective method and subjective method. Among them, the objective method has been widely used because of its objectivity and repeatability [21], [22]. According to whether undistorted reference images are required during evaluation, objective methods can generally be divided into full-reference IQA (FR-IQA) [23], reduced-reference IQA (RR-IQA) [24], and no-reference IQA (NR-IQA) [25].…”

Section: A Video Denoising Of Pipeline Leakage Based On Iqamentioning

confidence: 99%

High Reliability Pipeline Leakage Detection Based on Machine Vision in Complex Industrial Environment

Lyu

Zhang

et al. 2022

IEEE Sensors J.

View full text Add to dashboard Cite

The vision-based pipeline leakage detection is an intelligent leak detection method based on the industrial Internet of Things monitoring platform. It has the advantages of high safety factor and detection visualization. However, in the actual complex industrial environment, there are some problems, such as environmental interference and transmission quality of sensing networks. These low-quality sensing image data bring noise to the vision-based pipeline leakage detection, resulting in the risk of missed detection and false detection. In view of the above problems, we propose a highly reliable pipeline leakage detection method based on machine vision in the complex industrial environment. First, we propose a key frame selection method based on a lightweight image quality assessment, which can adapt to the complex detection environment. The key frame containing available feature information is selected to eliminate the interference of low-quality sensing image data on subsequent feature extraction and realize pipeline leakage video denoising. Then, the C3D network is used to extract space-time features at the same time to detect the leakage of pipeline leakage video. The experimental results show that the effect of our proposed method is better than other existing methods in the complex industrial environment. When the noisy data ratio of the detected image is 15%, the accuracy can be improved by 2.6 percentage points, up to 97.8%, which ensures the reliability of the pipeline leakage detection in the complex industrial environment.

show abstract

“…There are still significant distortions in underwater images enhanced by these methods. Besides, underwater image enhancement methods based on the degradation model have been proposed (Cui et al, 2022;Luo et al, 2022). These methods necessitate the construction of an appropriate degradation model for underwater images, followed by the restoration of the original, undegraded underwater image by simulating the degradation process.…”

Section: Introductionmentioning

confidence: 99%

Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement

Wang,

Zhao,

Zhong

et al. 2023

Front. Mar. Sci.

View full text Add to dashboard Cite

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods.

show abstract

A referenceless image degradation perception method based on the underwater imaging model

Cited by 9 publications

References 31 publications

Underwater Image Enhancement Based on Color Correction and Detail Enhancement

Underwater Image Enhancement Based on Color Correction and Detail Enhancement

High Reliability Pipeline Leakage Detection Based on Machine Vision in Complex Industrial Environment

Generative adversarial networks with multi-scale and attention mechanisms for underwater image enhancement

Contact Info

Product

Resources

About