scholarly journals Underwater image enhancement via LBP‐based attention residual network

2021 ◽  
Author(s):  
ZhiXiong Huang ◽  
Jinjiang Li ◽  
Zhen Hua
Keyword(s):  
Image Enhancement ◽  
Residual Network ◽  
Underwater Image

scholarly journals Underwater image enhancement via efficient generative adversarial network

2021 ◽  
Vol 51 (4) ◽  
Author(s):  
Xin Qian ◽  
Peng Ge
Keyword(s):  
Image Enhancement ◽  
Loss Function ◽  
State Of The Art ◽  
Residual Network ◽  
Kernel Size ◽  
Generative Adversarial Network ◽  
Qualitative And Quantitative ◽  
Adversarial Network ◽  
Underwater Image ◽  
Residual Block

Underwater image enhancement has been receiving much attention due to its significance in facilitating various marine explorations. Inspired by the generative adversarial network (GAN) and residual network (ResNet) in many vision tasks, we propose a simplified designed ResNet model based on GAN called efficient GAN (EGAN) for underwater image enhancement. In particular, for the generator of EGAN we design a new pair of convolutional kernel size for the residual block in the ResNet. Secondly, we abandon batch normalization (BN) after every convolution layer for faster training and less artifacts. Finally, a smooth loss function is introduced for halo-effect alleviation. Extensive qualitative and quantitative experiments show that our methods accomplish considerable improvements compared to the state-of-the-art methods.

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 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.

Underwater Image Enhancement Method via the Fusion of Retinex and Pulse Coupled Neural Network

2020 ◽  
Author(s):  
Zhengzhong Gao ◽  
Junjun Chen ◽  
Lixing Yang ◽  
Ruixing Lv ◽  
Haipeng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Pulse Coupled Neural Network ◽  
Underwater Image ◽  
Enhancement Method

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 An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

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