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.

Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement

2018 ◽  
Vol 32 (07) ◽  
pp. 1854018 ◽  
Author(s):  
Jinxiang Ma ◽  
Xinnan Fan ◽  
Simon X. Yang ◽  
Xuewu Zhang ◽  
Xifang Zhu
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Histogram Equalization ◽  
Color Spaces ◽  
Fusion Algorithm ◽  
Noise Interference ◽  
Adaptive Histogram Equalization ◽  
Rgb Images ◽  
Color Cast ◽  
Three Components

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. 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, using a 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 the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

scholarly journals An Enhancement of Underwater Images using DCP and CLAHE Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 2805-2813
Keyword(s):  
Image Enhancement ◽  
Correlation Method ◽  
Histogram Equalization ◽  
Experimental Result ◽  
Dark Channel Prior ◽  
Resource Requirement ◽  
Adaptive Histogram Equalization ◽  
Underwater Image ◽  
Dark Channel ◽  
Rgb Image

The lack of resource requirement in this population world, we are in a position to require another resources. In this regard, ocean is one of our sustenance. It is the exact platform for various applications like, transport, food, energy etc., but still we are surveyed partly at all aspects. One of the main focus of challenge is scattering of light as it penetrate from air to water which presents us with a bluish background while studying the scenery. In this, added to this there is a hazy appearance in the visuals and calls for Image Enhancement techniques. Here, Dark Channel Prior(DCP) is used to remove the haze and noise induced by the bluish environment. However, this proposal of method is also used to increase darkness of the image, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used on the RGB image to enhance the contrast and intensity of the image. Finally, we get visually pleasing result, colour correlation method is carried out. The experimental result shows that a enhanced underwater image from the base image, and mostly useful to analyze and monitoring the underwater images.

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 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

scholarly journals Hue-Preserving Saturation Improvement in RGB Color Cube

2021 ◽  
Vol 7 (8) ◽  
pp. 150
Author(s):  
Kohei Inoue ◽  
Minyao Jiang ◽  
Kenji Hara
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Color Image Enhancement ◽  
Histogram Specification ◽  
Target Intensity ◽  
Rgb Color Space ◽  
Intensity Image

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.

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