Multi-scale retinex-based adaptive gray-scale transformation method for underwater image enhancement

2021 ◽  
Author(s):  
Jingchun Zhou ◽  
Jian Yao ◽  
Weishi Zhang ◽  
Dehuan Zhang
Keyword(s):  
Image Enhancement ◽  
Transformation Method ◽  
Scale Transformation ◽  
Gray Scale ◽  
Multi Scale ◽  
Underwater Image

scholarly journals An improved gray-scale transformation method for pseudo-color image enhancement

2019 ◽  
Vol 43 (1) ◽  
pp. 78-82 ◽  
Author(s):  
H. Gao ◽  
◽  
W. Zeng ◽  
J. Chen ◽  
◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Transformation Method ◽  
Scale Transformation ◽  
Gray Scale ◽  
Color Image Enhancement ◽  
Pseudo Color

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.

scholarly journals Underwater Image Enhancement Based on Global and Local Equalization of Histogram and Dual-Image Multi-Scale Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 128973-128990
Author(s):  
Linfeng Bai ◽  
Weidong Zhang ◽  
Xipeng Pan ◽  
Chenping Zhao
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Underwater Image ◽  
Dual Image ◽  
Global And Local

scholarly journals Underwater image enhancement via extended multi-scale Retinex

2017 ◽  
Vol 245 ◽  
pp. 1-9 ◽  
Author(s):  
Shu Zhang ◽  
Ting Wang ◽  
Junyu Dong ◽  
Hui Yu
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Underwater Image

scholarly journals Multi-scale fusion for underwater image enhancement using multi-layer perceptron

2021 ◽  
Vol 10 (2) ◽  
pp. 389
Author(s):  
M. Sudhakara ◽  
M. Janaki Meena
Keyword(s):  
Image Enhancement ◽  
Light Attenuation ◽  
Computational Time ◽  
Multi Layer Perceptron ◽  
Step Method ◽  
Multi Scale ◽  
Underwater Image ◽  
Dark Channel ◽  
Quantitative Results ◽  
The Difference

<span id="docs-internal-guid-54b35aa6-7fff-0992-ed4c-aca4d05cfcfa"><span>Underwater image enhancement (UIE) is an imperative computer vision activity with many applications and different strategies proposed in recent years. Underwater images are firmly low in quality by a mixture of noise, wavelength dependency, and light attenuation. This paper depicts an effective strategy to improve the quality of degraded underwater images. Existing methods for dehazing in the literature considering dark channel prior utilize two separate phases for evaluating the transmission map (i.e., transmission estimation and transmission refinement). Accurate restoration is not possible with these methods and takes more computational time. A proposed three-step method is an imaging approach that does not need particular hardware or underwater conditions. First, we utilize the multi-layer perceptron (MLP) to comprehensively evaluate transmission maps by base channel, followed by contrast enhancement.  Furthermore, a gamma-adjusted version of the MLP recovered image is derived. Finally, the multi-scale fusion method was applied to two attained images. The standardized weight is computed for the two images with three different weights in the fusion process. The quantitative results show that significantly our approach gives the better result with the difference of 0.536, 2.185, and 1.272 for PCQI, UCIQE, and UIQM metrics, respectively, on a single underwater image benchmark dataset. The qualitative results also give better results compared with the state-of-the-art techniques.</span></span>

Multi-Scale Gradient Domain Underwater Image Enhancement

2018 ◽  
Author(s):  
Zetian Mi ◽  
Zheng Liang ◽  
Yafei Wang ◽  
Xianping Fu ◽  
Zhengyu Chen
Keyword(s):  
Image Enhancement ◽  
Multi Scale ◽  
Underwater Image ◽  
Gradient Domain
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