Underwater image enhancement using improved generative adversarial network

2020 ◽  
Author(s):  
Tingting Zhang ◽  
Yujie Li ◽  
Shinya Takahashi
Keyword(s):  
Image Enhancement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image

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.

Underwater Image Enhancement Based on Conditional Generative Adversarial Network

2020 ◽  
Vol 57 (14) ◽  
pp. 141002
Author(s):  
晋玮佩 Jin Weipei ◽  
郭继昌 Guo Jichang ◽  
祁清 Qi Qing
Keyword(s):  
Image Enhancement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image

Underwater image enhancement method based on the generative adversarial network

2021 ◽  
Vol 30 (01) ◽  
Author(s):  
Jin-Tao Yu ◽  
Rui-Sheng Jia ◽  
Li Gao ◽  
Ruo-Nan Yin ◽  
Hong-Mei Sun ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image ◽  
Enhancement Method

scholarly journals Unpaired Underwater Image Enhancement Based on CycleGAN

Information ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Rong Du ◽  
Weiwei Li ◽  
Shudong Chen ◽  
Congying Li ◽  
Yong Zhang
Keyword(s):  
Image Enhancement ◽  
Paired Data ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Underwater Image ◽  
Training Stage ◽  
Proposed Model ◽  
Degraded Image ◽  
Enhancement Method ◽  
Degraded Images

Underwater image enhancement recovers degraded underwater images to produce corresponding clear images. Image enhancement methods based on deep learning usually use paired data to train the model, while such paired data, e.g., the degraded images and the corresponding clear images, are difficult to capture simultaneously in the underwater environment. In addition, how to retain the detailed information well in the enhanced image is another critical problem. To solve such issues, we propose a novel unpaired underwater image enhancement method via a cycle generative adversarial network (UW-CycleGAN) to recover the degraded underwater images. Our proposed UW-CycleGAN model includes three main modules: (1) A content loss regularizer is adopted into the generator in CycleGAN, which constrains the detailed information existing in one degraded image to remain in the corresponding generated clear image; (2) A blur-promoting adversarial loss regularizer is introduced into the discriminator to reduce the blur and noise in the generated clear images; (3) We add the DenseNet block to the generator to retain more information of each feature map in the training stage. Finally, experimental results on two unpaired underwater image datasets produced satisfactory performance compared to the state-of-the-art image enhancement methods, which proves the effectiveness of the proposed model.

Underwater image enhancement based on conditional generative adversarial network

2020 ◽  
Vol 81 ◽  
pp. 115723 ◽  
Author(s):  
Miao Yang ◽  
Ke Hu ◽  
Yixiang Du ◽  
Zhiqiang Wei ◽  
Zhibin Sheng ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Generative Adversarial Network ◽  
Adversarial 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.

scholarly journals Enhanced network optimized generative adversarial network for image enhancement

2021 ◽  
Author(s):  
Lingyu Yan ◽  
Jiarun Fu ◽  
Chunzhi Wang ◽  
Zhiwei Ye ◽  
Hongwei Chen ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Image Recognition ◽  
Generative Adversarial Networks ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Enhancement Method ◽  
New Space ◽  
Traditional Image

AbstractWith the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

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