Deep generative adversarial networks for infrared image enhancement

2018 ◽  
Author(s):  
Moulay A. Akhloufi ◽  
Axel-Christian Guei
Keyword(s):  
Image Enhancement ◽  
Infrared Image ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

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.

scholarly journals Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

2019 ◽  
Vol 7 (7) ◽  
pp. 200 ◽  
Author(s):  
Jaihyun Park ◽  
David K. Han ◽  
Hanseok Ko
Keyword(s):  
Image Enhancement ◽  
Real World ◽  
Input Image ◽  
Generative Adversarial Networks ◽  
Synthetic Image ◽  
Training Procedure ◽  
Weighting Method ◽  
Adversarial Networks ◽  
Underwater Image ◽  
Enhancement Method

In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unrealistic images with less content information than original images. A novel solution we propose here is by starting with a CycleGAN, we add a pair of discriminators to preserve contents of input image while enhancing the image. As a part of the solution, we introduce an adaptive weighting method for limiting losses of the two types of discriminators to balance their influence and stabilize the training procedure. Extensive experiments demonstrate that the proposed method significantly outperforms the state-of-the-art methods on real-world underwater images.

scholarly journals Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82819-82831
Author(s):  
Songkui Chen ◽  
Daming Shi ◽  
Muhammad Sadiq ◽  
Xiaochun Cheng
Keyword(s):  
Image Enhancement ◽  
Image Denoising ◽  
Generative Adversarial Networks ◽  
Cell Image ◽  
Adversarial Networks

scholarly journals I-GANs for Infrared Image Generation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Bing Li ◽  
Yong Xian ◽  
Juan Su ◽  
Da Q. Zhang ◽  
Wei L. Guo
Keyword(s):  
Infrared Imaging ◽  
Random Noise ◽  
Infrared Image ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Discriminant Model ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Network Training ◽  
Improved Learning

The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming. In order to address this problem, an infrared image generation method, infrared generative adversarial networks (I-GANs), based on conditional generative adversarial networks (CGAN) architecture is proposed. In I-GANs, visible images instead of random noise are used as the inputs, and the D-LinkNet network is also utilized to build the generative model, enabling improved learning of rich image textures and identification of dependencies between images. Moreover, the PatchGAN architecture is employed to build a discriminant model to process the high-frequency components of the images effectively and reduce the amount of calculation required. In addition, batch normalization is used to optimize the training process, and thereby, the instability and mode collapse of the generated adversarial network training can be alleviated. Finally, experimental verification is conducted on the produced infrared/visible light dataset (IVFG). The experimental results reveal that high-quality and reliable infrared data are generated by the proposed I-GANs.

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