Adaptive Stretching Method for Underwater Image Color Correction

The physical properties of water lead to attenuation of light that travels through the water channel. The attenuation is dependent on the color spectrum wavelength, that results in low contrast and color cast in image acquisition. Several methods have been proposed to handle these problems, such as Linear Stretching, Histogram Equalization (HE) and their variants. Considering the advantages of HE and Linear Stretching, this paper presents a new Adaptive Linear Stretch method (ALS) which can efficiently improve the subjective impression of the traditional Linear Stretching and keep the computational cost low at the same time. To achieve adaptability, the adaptable threshold is deduced from the histogram of image. Performance analysis reveals that the proposed method significantly enhances the image contrast, reduces the color cast and meanwhile, keeps the computational consumption low.

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Multiscale Fusion Method for the Enhancement of Low-Light Underwater Images

Mathematical Problems in Engineering ◽

10.1155/2020/7095248 ◽

2020 ◽

Vol 2020 ◽

pp. 1-15

Author(s):

Jingchun Zhou ◽

Dehuan Zhang ◽

Weishi Zhang

Keyword(s):

High Frequency ◽

Low Frequency ◽

Histogram Equalization ◽

Gradient Field ◽

Low Contrast ◽

Frequency Information ◽

Underwater Image ◽

Enhancement Method ◽

Quantitative Performance ◽

Color Cast

To solve the color cast and low contrast of underwater images caused by the effects of light absorption and scattering, we propose a novel underwater image enhancement method via bi-interval histogram equalization. The proposed method consists of three main parts: color correction, contrast enhancement, and multiscale fusion. First, the color cast is eliminated by automatic white balancing. Then, homomorphic filtering is adopted to decompose the image into high-frequency information and low-frequency information, the high-frequency information is enhanced by the gradient field bi-interval equalization which enhances the contrast and details of the image, and the low-frequency information is disposed via gamma correction for adjusting the exposure. Finally, we adopt a multiscale fusion strategy to fuse the high-frequency information, high-frequency after bi-interval equalization, and low-frequency information based on contrast, saturation, and exposure. Qualitative and quantitative performance evaluations demonstrate that the proposed method can effectively enhance the details and global contrast of the image and achieve better exposedness of the dark areas, which outperforms several state-of-the-art methods.

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Underwater Image Enhancement Using Improved CNN Based Defogging

Electronics ◽

10.3390/electronics11010150 ◽

2022 ◽

Vol 11 (1) ◽

pp. 150

Author(s):

Meicheng Zheng ◽

Weilin Luo

Keyword(s):

Histogram Equalization ◽

Input Image ◽

Fusion Algorithm ◽

Low Contrast ◽

Network Training ◽

Underwater Image ◽

Comparison Results ◽

Color Restoration ◽

End To End ◽

Lab Color Model

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.

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An improved K-means algorithm for underwater image background segmentation

Multimedia Tools and Applications ◽

10.1007/s11042-021-10693-7 ◽

2021 ◽

Author(s):

Wei Chen ◽

Cenyu He ◽

Chunlin Ji ◽

Meiying Zhang ◽

Siyu Chen

Keyword(s):

The Other ◽

Grayscale Image ◽

K Value ◽

Low Contrast ◽

Underwater Image ◽

Background Segmentation ◽

Value Determination ◽

Color Cast ◽

The Impact ◽

Centroid Position

AbstractConventional algorithms fail to obtain satisfactory background segmentation results for underwater images. In this study, an improved K-means algorithm was developed for underwater image background segmentation to address the issue of improper K value determination and minimize the impact of initial centroid position of grayscale image during the gray level quantization of the conventional K-means algorithm. A total of 100 underwater images taken by an underwater robot were sampled to test the aforementioned algorithm in respect of background segmentation validity and time cost. The K value and initial centroid position of grayscale image were optimized. The results were compared to the other three existing algorithms, including the conventional K-means algorithm, the improved Otsu algorithm, and the Canny operator edge extraction method. The experimental results showed that the improved K-means underwater background segmentation algorithm could effectively segment the background of underwater images with a low color cast, low contrast, and blurred edges. Although its cost in time was higher than that of the other three algorithms, it none the less proved more efficient than the time-consuming manual segmentation method. The algorithm proposed in this paper could potentially be used in underwater environments for underwater background segmentation.

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Wavelet Techniques for Medical Images Performance Analysis and Observations with EZW and Underwater Image Processing

Wireless Personal Communications ◽

10.1007/s11277-020-07238-w ◽

2020 ◽

Author(s):

Javed Miya ◽

M. A. Ansari

Keyword(s):

Image Processing ◽

Performance Analysis ◽

Medical Images ◽

Underwater Image ◽

Wavelet Techniques ◽

Underwater Image Processing

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Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

Journal of Marine Science and Engineering ◽

10.3390/jmse9020225 ◽

2021 ◽

Vol 9 (2) ◽

pp. 225

Author(s):

Farong Gao ◽

Kai Wang ◽

Zhangyi Yang ◽

Yejian Wang ◽

Qizhong Zhang

Keyword(s):

Image Enhancement ◽

Local Contrast ◽

Fusion Method ◽

Formation Model ◽

Low Contrast ◽

White Balance ◽

Multi Scale ◽

Color Distortion ◽

Underwater Image ◽

Enhancement Method

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

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Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images

Applied Sciences ◽

10.3390/app10186392 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6392

Author(s):

Xieliu Yang ◽

Chenyu Yin ◽

Ziyu Zhang ◽

Yupeng Li ◽

Wenfeng Liang ◽

...

Keyword(s):

Color Space ◽

Color Correction ◽

Test Methods ◽

Chromatic Adaptation ◽

Water Type ◽

Underwater Image ◽

Other Information ◽

Color Restoration ◽

Color Cast ◽

Imaging Conditions

Recovering correct or at least realistic colors of underwater scenes is a challenging issue for image processing due to the unknown imaging conditions including the optical water type, scene location, illumination, and camera settings. With the assumption that the illumination of the scene is uniform, a chromatic adaptation-based color correction technology is proposed in this paper to remove the color cast using a single underwater image without any other information. First, the underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels. Second, the illumination is estimated in a uniform chromatic space based on the white-patch hypothesis. Third, the chromatic adaptation transform is implemented in the device-independent XYZ color space. Qualitative and quantitative evaluations both show that the proposed method outperforms the other test methods in terms of color restoration, especially for the images with severe color cast. The proposed method is simple yet effective and robust, which is helpful in obtaining the in-air images of underwater scenes.

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