scholarly journals Enhancement Underwater Image Using Histogram Equalization Based on Color Restoration

2019 ◽  
Vol 14 (2) ◽  
pp. 641-647
Author(s):  
Zahraa S. Abd-Al Ameer ◽  
Hazim G. Daway ◽  
Hana H. Kareem
Keyword(s):  
Histogram Equalization ◽  
Underwater Image ◽  
Color Restoration

scholarly journals Underwater Image Enhancement Using Improved CNN Based Defogging

Electronics ◽  
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.

scholarly journals Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images

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.

A Modified Multi Scale Retinex with Color Restoration Algorithm for Automatic Enhancement of Landsat-5 Remote Sensing Image

2011 ◽  
Vol 341-342 ◽  
pp. 893-897
Author(s):  
Gui Zhou Wang ◽  
Guo Jin He
Keyword(s):  
Image Enhancement ◽  
Dynamic Range ◽  
Color Constancy ◽  
Human Perception ◽  
Histogram Equalization ◽  
Image Processing Algorithm ◽  
Multi Scale ◽  
Dynamic Range Compression ◽  
Restoration Algorithm ◽  
Color Restoration

The retinex is a human perception based image processing algorithm which provides color constancy and dynamic range compression. The multi scale retinex with color restoration (MSRCR) has shown itself to be a very versatile automatic image enhancement algorithm that simultaneously provides dynamic range compression, color constancy, and color rendition. But the MSRCR results suffer from lower global brightness and partial color distortion. In order to improve the MSRCR method, this paper presents a modified MSRCR algorithm to Landsat-5 image enhancement considering percent liner stretch and histogram adjustment. Finally, the effect of modified MSRCR method on Landsat-5 image enhancement is analyzed and the comparison with other color adjustment methods such as gamma correction and histogram equalization is reported in the experimental results.

A Correction Algorithm of Multi-View Image System

2014 ◽  
Vol 631-632 ◽  
pp. 395-398
Author(s):  
Xin Yang Wang ◽  
Huan Xia Feng ◽  
Jian Zhang
Keyword(s):  
Light Source ◽  
Color Constancy ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Image Correction ◽  
Correction Algorithm ◽  
Intrinsic Properties ◽  
Image System ◽  
Correction Effect ◽  
Color Restoration

This paper, based on the Retinex Color Constancy Theory, proposes a novel multi-view image correction algorithm, which correction effect is very effective. Histogram equalization,retinex processing and color restoration are performed for multi-view images,and extract reflectance which describe object intrinsic properties to eliminate un-consistent light source influence.The experimental results show that corrected images not only have high color contract,also have consistent color appearance with others.

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

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