Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage

2021 ◽  
pp. 1-13
Author(s):  
Daxin Zhou ◽  
Yurong Qian ◽  
Yuanyuan Ma ◽  
Yingying Fan ◽  
Jianeng Yang ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Image Features ◽  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Network Methods ◽  
Convolution Kernels ◽  
Low Illumination ◽  
Noise Amplification

Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising.

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 A Multi-Resolution Approach to GAN-Based Speech Enhancement

2021 ◽  
Vol 11 (2) ◽  
pp. 721
Author(s):  
Hyung Yong Kim ◽  
Ji Won Yoon ◽  
Sung Jun Cheon ◽  
Woo Hyun Kang ◽  
Nam Soo Kim
Keyword(s):  
Speech Enhancement ◽  
Optimal Solution ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
The Real ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Speech Characteristics ◽  
Conventional Methods ◽  
Convex Property

Recently, generative adversarial networks (GANs) have been successfully applied to speech enhancement. However, there still remain two issues that need to be addressed: (1) GAN-based training is typically unstable due to its non-convex property, and (2) most of the conventional methods do not fully take advantage of the speech characteristics, which could result in a sub-optimal solution. In order to deal with these problems, we propose a progressive generator that can handle the speech in a multi-resolution fashion. Additionally, we propose a multi-scale discriminator that discriminates the real and generated speech at various sampling rates to stabilize GAN training. The proposed structure was compared with the conventional GAN-based speech enhancement algorithms using the VoiceBank-DEMAND dataset. Experimental results showed that the proposed approach can make the training faster and more stable, which improves the performance on various metrics for speech enhancement.

scholarly journals Parkinson’s Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2605 ◽  
Author(s):  
Rafael Anicet Zanini ◽  
Esther Luna Colombini
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Augmentation ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Treatment Approaches ◽  
Adversarial Networks

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, simulating each patient’s tremor patterns and extending them to different sets of movement protocols. Therefore, one could use these models for extending the existing patient dataset and generating tremor simulations for validating treatment approaches on different movement scenarios.

scholarly journals Research on image style transfer method based on Generative Adversarial Networks

2019 ◽  
Vol 1314 ◽  
pp. 012191
Author(s):  
Jie Wu ◽  
Jin Duan ◽  
Jinqiang Yu ◽  
Haodong Shi ◽  
Yingchao Li
Keyword(s):  
Generative Adversarial Networks ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Transfer Method
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