scholarly journals Underwater Image Enhancement by Adaptive Gray World and Differential Gray-Levels Histogram Equalization

2018 ◽  
Vol 18 (2) ◽  
pp. 109-116 ◽  
Author(s):  
S.-L. WONG ◽  
R. PARAMESRAN ◽  
A. TAGUCHI
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Underwater Image

scholarly journals Deep Learning Approach for Submerged Image Enhancement

2021 ◽  
Vol 9 (10) ◽  
pp. 214-219
Author(s):  
Dr. Geeta Hanji
Keyword(s):  
Deep Learning ◽  
Image Enhancement ◽  
Light Attenuation ◽  
Histogram Equalization ◽  
Color Contrast ◽  
Learning Approach ◽  
Gamma Correction ◽  
Ocean Engineering ◽  
White Balance ◽  
Underwater Image

Abstract: Because of underwater pictures application in ocean engineering, ocean research, marine biology, and marine archaeology to name a few, underwater picture enhancement was widely publicized in the last several years. Underwater photos frequently upshot in low contrast, blurred, color distortion, hazy, poor visible images. This is because of light attenuation, absorption, scattering (forward scattering and backward scattering), turbidity, floating particles. As a result, effective underwater picture solution must be developedin order to improve visibility, contrast, and color qualities for greater visual quality and optical attractiveness. Many underwater picture enhancing approaches have been proposed to overcome these challenges; however they all failed to produce accurate results. Hence for this we first undertook a large scale underwater image dataset which is trained by convolution neural network (CNN) and then we have studied and implemented a deep learning approach called very deep super resolution (VDSR) model for improving the color, contrast, and brightness of underwater photos by using different algorithms such as white balance, histogram equalization, and gamma correction respectively. Moreover, our method is compared with the existing method which reveals that our method surpassesthe existing methods Keywords: CNN, gamma correction, histogram equalization, underwater image enhancement, VDSR, white balance

scholarly journals Image Enhancement using Recursive Standard Intensity Deviation Based Clipped Sub Image Histogram Equalization

2019 ◽  
Vol 9 (2) ◽  
pp. 3933-3937
Keyword(s):  
Image Enhancement ◽  
Histogram Equalization ◽  
Light Conditions ◽  
Low Light ◽  
Image Histogram ◽  
Output Image ◽  
Standard Intensity ◽  
Underwater Image ◽  
Image Inspection ◽  
Low Exposure

The low exposure image enhancement has become indispensable inimage processing for better visibility. The most challenging in image enhancement is especially to curtail overenhancement problems. This paper presents a method, performs the separation of the histogram based on respective standard intensity deviation value and then recursively equalizes all sub histograms independently. The over-enhancement problem is minimized by this method. It applies more in an underwater image, because of its low light conditions. The experiment results are analyzed in terms of entropy and output image inspection. The proposed method results show significant improvement over earlier recursive based histogram equalization algorithms.

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.

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