scholarly journals Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

2021 ◽  
pp. 1-1
Author(s):  
Chongyi Li ◽  
Saeed Anwar ◽  
Junhui Hou ◽  
Runmin Cong ◽  
Chunle Guo ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Underwater Image

Underwater Image Enhancement Method Combining color and Luminance

2020 ◽  
Vol 13 ◽  
Author(s):  
ZHAO Baiting ◽  
WANG Feng ◽  
JIA Xiaofen ◽  
GUO Yongcun ◽  
WANG Chengjun
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Low Frequency ◽  
Brightness Enhancement ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method ◽  
Frequency Components ◽  
Image Contour ◽  
Objective Indicators

Background:: Aiming at the problems of color distortion, low clarity and poor visibility of underwater image caused by complex underwater environment, a wavelet fusion method UIPWF for underwater image enhancement is proposed. Methods:: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels, and balance the color of R, G and B channels by affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results:: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion:: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

scholarly journals Adaptive Cubic Spline Interpolation in CIELAB Color Space for Underwater Image Enhancement

2020 ◽  
Vol 171 ◽  
pp. 52-61
Author(s):  
Deepti Hegde ◽  
Chaitra Desai ◽  
Ramesh Tabib ◽  
Ujwala B. Patil ◽  
Uma Mudenagudi ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Spline Interpolation ◽  
Color Space ◽  
Cubic Spline ◽  
Cubic Spline Interpolation ◽  
Cielab Color Space ◽  
Underwater Image ◽  
Cielab Color

scholarly journals UIEĈ2-Net: CNN-based underwater image enhancement using two color space

2021 ◽  
pp. 116250
Author(s):  
Yudong Wang ◽  
Jichang Guo ◽  
Huan Gao ◽  
Huihui Yue
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Underwater Image

Underwater Color Image Enhancement Using Combining Schemes

2014 ◽  
Vol 48 (3) ◽  
pp. 57-62 ◽  
Author(s):  
Xin Luan ◽  
Guojia Hou ◽  
Zhengyuan Sun ◽  
Yongfang Wang ◽  
Dalei Song ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Color Contrast ◽  
Blue Color ◽  
Wavelet Domain ◽  
Nonuniform Illumination ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

AbstractUnderwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.

scholarly journals Contrast Limited Adaptive Histogram Equalization Based Fusion for Underwater Image Enhancement

2017 ◽  
Author(s):  
JINXIANG MA ◽  
Xinnan Fan ◽  
Simon X. Yang ◽  
Xuewu Zhang ◽  
Xifang Zhu
Keyword(s):  
Image Enhancement ◽  
Color Space ◽  
Histogram Equalization ◽  
Color Spaces ◽  
Edge Detector ◽  
Fusion Algorithm ◽  
Adaptive Histogram Equalization ◽  
Underwater Image ◽  
Rgb Images ◽  
Three Components

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

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

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.

scholarly journals Underwater Image Enhancement Based on Multi-Scale Fusion and Global Stretching of Dual-Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 595
Author(s):  
Huajun Song ◽  
Rui Wang
Keyword(s):  
Image Enhancement ◽  
Poor Performance ◽  
Weight Coefficient ◽  
Color Contrast ◽  
Dual Model ◽  
Spatial Cues ◽  
Multi Scale ◽  
Imaging Model ◽  
Underwater Image ◽  
Enhancement Method

Aimed at the two problems of color deviation and poor visibility of the underwater image, this paper proposes an underwater image enhancement method based on the multi-scale fusion and global stretching of dual-model (MFGS), which does not rely on the underwater optical imaging model. The proposed method consists of three stages: Compared with other color correction algorithms, white-balancing can effectively eliminate the undesirable color deviation caused by medium attenuation, so it is selected to correct the color deviation in the first stage. Then, aimed at the problem of the poor performance of the saliency weight map in the traditional fusion processing, this paper proposed an updated strategy of saliency weight coefficient combining contrast and spatial cues to achieve high-quality fusion. Finally, by analyzing the characteristics of the results of the above steps, it is found that the brightness and clarity need to be further improved. The global stretching of the full channel in the red, green, blue (RGB) model is applied to enhance the color contrast, and the selective stretching of the L channel in the Commission International Eclairage-Lab (CIE-Lab) model is implemented to achieve a better de-hazing effect. Quantitative and qualitative assessments on the underwater image enhancement benchmark dataset (UIEBD) show that the enhanced images of the proposed approach achieve significant and sufficient improvements in color and visibility.

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