Underwater Image Enhancement Based on Color Correction and Detail Enhancement

Wu, Zeju; Ji, Yang; Song, Liang; Sun, Jie

doi:10.3390/jmse10101513

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…CAM improves attention to the target category, and SAM improves attention to the target location. 21 Channel attention focuses on the weighted information of different channels for global average pooling, with the number of channels remaining the same and changing the height and width to one. 22 The equation for channel attention is as follows:…”

Section: Convolutional Block Attention Modulementioning

confidence: 99%

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Underwater image enhancement based on a combination of improved gated context aggregation network and gray world algorithms

Liu,

Hong,

Gao

2024

J. Electron. Imag.

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Underwater images received by underwater robots in unrestricted environment during underwater operations are characterized by overall bluish and greenish tones, blurrier edge details, and low contrast. This phenomenon is due to the attenuation and scattering of light in the water and the influence of artificial light sources. To improve the visual performance of underwater imaging, we propose a two-step method of improved gated context aggregation network and gray world algorithms. First, based on similarities between the underwater optical imaging model and the atmosphere model, a gray world algorithm is used to calibrate the image. Then, the corrected underwater images are inputted into an improved gated contextual aggregation network, which is utilized to fuse the features at different levels within the images. The introduction of convolutional block attention module and residual structure can effectively improve the feature extraction ability and prevent network degradation. The method eliminates the grid artifact phenomenon and improves the flexibility of channel information to achieve image enhancement. By conducting experiments, we then compare the performance of the proposed method with six classical state-of-the-art methods. The qualitative results confirm that the proposed method is capable of effectively removing haze, correcting the color deviation of underwater images, and maintaining the naturalness of images. We further perform a quantitative evaluation and show that the proposed method outperforms the other methods that are compared with the proposed in terms of peak signal-to-noise ratio and structural similarity . The enhancement results are also measured by employing the information entropy and underwater color image quality evaluation index, indicating that the proposed method exhibits the highest mean values of 8.0579, 8.1194, and 0.6182, 0.5914 on the two datasets. The experimental results collectively validate the effectiveness of the proposed method in improved underwater image blur.

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Section: Convolutional Block Attention Modulementioning

confidence: 99%

“…6, the feature map goes through CAM and then SAM. CAM improves attention to the target category, and SAM improves attention to the target location 21 …”

Section: Generative Adversarial Network Based On Gated Aggregationmentioning

confidence: 99%

Underwater image enhancement based on a combination of improved gated context aggregation network and gray world algorithms

Liu,

Hong,

Gao

2024

J. Electron. Imag.

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“…The White Balance Equation can be used to alter the colour balance of underwater photographs and correct for colour distortion brought on by water absorption, according to a study by Kawaguchi. [9] R_corr = R / R_w * R_gw…”

Section: B Color Distortionmentioning

confidence: 99%

Underwater Image Enhancement - An Accessible Solution

Gupta,

Bhadauria,

2023

Recent Trends in Data Science and Its Applications

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Underwater optical images are insufficient for visual examination or analysis due to their poor colour representation, low contrast, and excessive noise levels. This project suggests a novel method of underwater image improvement that includes colour correction, image defogging/dehazing, image denoising, and object recognition in order to overcome these difficulties.The procedure relies on the image_dehazer python library for dehazing, the fastNlMeansDenoisingColored function of OpenCV for denoising, and the Google vision API for object detection. Based on the input image, a filter matrix is created to correct the colour by adjusting the hue and normalising the red, green, and blue channel intensities. The suggested technique is put into practice using a flask API that is hosted on the internet and usable on low-end computers and mobile devices. In contrast to existing approaches, this technology is highly effective and appropriate for a variety of underwater imaging applications because it does not rely on machine learning or training models.This research presents an effective method for improving underwater images, suitable for various imaging applications. The sample size of the images used was 890 (Large Scale Underwater Image Dataset). The temporal efficiency of this code is significantly influenced by the dimensions of the input image and the intricacy of the filter matrix. It has potential use in submarine operations for defense purposes such as surveillance and monitoring. The proposed method produces high-quality results while being accessible and cost-effective.

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“…Existing underwater restoration methods are mostly based on the image formation model (IFM) [ 6 , 7 , 8 ], which describes the linear relationship among the direct component, the forward scattering component, and the backward scattering component. In this model, two crucial parameters, the background lights and the transmission map, were estimated to recover underwater haze-free images.…”

Section: Introductionmentioning

confidence: 99%

Enhancement and Optimization of Underwater Images and Videos Mapping

Xiang

Wang

et al. 2023

Sensors

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Underwater images tend to suffer from critical quality degradation, such as poor visibility, contrast reduction, and color deviation by virtue of the light absorption and scattering in water media. It is a challenging problem for these images to enhance visibility, improve contrast, and eliminate color cast. This paper proposes an effective and high-speed enhancement and restoration method based on the dark channel prior (DCP) for underwater images and video. Firstly, an improved background light (BL) estimation method is proposed to estimate BL accurately. Secondly, the R channel’s transmission map (TM) based on the DCP is estimated sketchily, and a TM optimizer integrating the scene depth map and the adaptive saturation map (ASM) is designed to refine the afore-mentioned coarse TM. Later, the TMs of G–B channels are computed by their ratio to the attenuation coefficient of the red channel. Finally, an improved color correction algorithm is adopted to improve visibility and brightness. Several typical image-quality assessment indexes are employed to testify that the proposed method can restore underwater low-quality images more effectively than other advanced methods. An underwater video real-time measurement is also conducted on the flipper-propelled underwater vehicle-manipulator system to verify the effectiveness of the proposed method in the real scene.

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Underwater Image Enhancement Based on Color Correction and Detail Enhancement

Cited by 6 publications

References 37 publications

Underwater image enhancement based on a combination of improved gated context aggregation network and gray world algorithms

Underwater image enhancement based on a combination of improved gated context aggregation network and gray world algorithms

Underwater Image Enhancement - An Accessible Solution

Enhancement and Optimization of Underwater Images and Videos Mapping

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