scholarly journals Contextual Information Aided Generative Adversarial Network for Low-Light Image Enhancement

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Shiyong Hu ◽  
Jia Yan ◽  
Dexiang Deng
Keyword(s):  
Image Enhancement ◽  
Contextual Information ◽  
Feature Interaction ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Local Distortion ◽  
Light Image ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Detail Loss

Low-light image enhancement has been gradually becoming a hot research topic in recent years due to its wide usage as an important pre-processing step in computer vision tasks. Although numerous methods have achieved promising results, some of them still generate results with detail loss and local distortion. In this paper, we propose an improved generative adversarial network based on contextual information. Specifically, residual dense blocks are adopted in the generator to promote hierarchical feature interaction across multiple layers and enhance features at multiple depths in the network. Then, an attention module integrating multi-scale contextual information is introduced to refine and highlight discriminative features. A hybrid loss function containing perceptual and color component is utilized in the training phase to ensure the overall visual quality. Qualitative and quantitative experimental results on several benchmark datasets demonstrate that our model achieves relatively good results and has good generalization capacity compared to other state-of-the-art low-light enhancement algorithms.

scholarly journals Low-Light Image Enhancement Based on Generative Adversarial Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Nandhini Abirami R. ◽  
Durai Raj Vincent P. M.
Keyword(s):  
Image Enhancement ◽  
Visual Information ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Light Image ◽  
Adversarial Network ◽  
Benchmark Datasets ◽  
Dim Light ◽  
Effective Network

Image enhancement is considered to be one of the complex tasks in image processing. When the images are captured under dim light, the quality of the images degrades due to low visibility degenerating the vision-based algorithms’ performance that is built for very good quality images with better visibility. After the emergence of a deep neural network number of methods has been put forward to improve images captured under low light. But, the results shown by existing low-light enhancement methods are not satisfactory because of the lack of effective network structures. A low-light image enhancement technique (LIMET) with a fine-tuned conditional generative adversarial network is presented in this paper. The proposed approach employs two discriminators to acquire a semantic meaning that imposes the obtained results to be realistic and natural. Finally, the proposed approach is evaluated with benchmark datasets. The experimental results highlight that the presented approach attains state-of-the-performance when compared to existing methods. The models’ performance is assessed using Visual Information Fidelitysse, which assesses the generated image’s quality over the degraded input. VIF obtained for different datasets using the proposed approach are 0.709123 for LIME dataset, 0.849982 for DICM dataset, 0.619342 for MEF dataset.

scholarly journals Low-light Image Enhancement Based on Conditional Generative Adversarial Network

2021 ◽  
Vol 2035 (1) ◽  
pp. 012027
Author(s):  
Huaji Li ◽  
Jianghua Cheng ◽  
Tong Liu ◽  
Bang Cheng ◽  
Zilong Liu
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Light Image ◽  
Adversarial Network

scholarly journals A Novel DenseNet Generative Adversarial Network for Heterogenous Low-Light Image Enhancement

2021 ◽  
Vol 15 ◽  
Author(s):  
Jingsi Zhang ◽  
Chengdong Wu ◽  
Xiaosheng Yu ◽  
Xiaoliang Lei
Keyword(s):  
Image Enhancement ◽  
Low Light ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Brightness ◽  
Light Image ◽  
Adversarial Network ◽  
Enhancement Method ◽  
The Rich ◽  
Normal Light

With the development of computer vision, high quality images with rich information have great research potential in both daily life and scientific research. However, due to different lighting conditions, surrounding noise and other reasons, the image quality is different, which seriously affects people's discrimination of the information in the image, thus causing unnecessary conflicts and results. Especially in the dark, the images captured by the camera are difficult to identify, and the smart system relies heavily on high-quality input images. The image collected in low-light environment has the characteristic with high noise and color distortion, which makes it difficult to utilize the image and can not fully explore the rich value information of the image. In order to improve the quality of low-light image, this paper proposes a Heterogenous low-light image enhancement method based on DenseNet generative adversarial network. Firstly, the generative network of generative adversarial network is realized by using DenseNet framework. Secondly, the feature map from low light image to normal light image is learned by using the generative adversarial network. Thirdly, the enhancement of low-light image is realized. The experimental results show that, in terms of PSNR, SSIM, NIQE, UQI, NQE and PIQE indexes, compared with the state-of-the-art enhancement algorithms, the values are ideal, the proposed method can improve the image brightness more effectively and reduce the noise of enhanced image.

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 Enhance Image as You Like with Unpaired Learning

2021 ◽  
Author(s):  
Xiaopeng Sun ◽  
Muxingzi Li ◽  
Tianyu He ◽  
Lubin Fan
Keyword(s):  
Learning Task ◽  
User Preferences ◽  
Generative Adversarial Networks ◽  
Low Light ◽  
Generative Adversarial Network ◽  
Light Image ◽  
Adversarial Network ◽  
Reference Images ◽  
Ill Posed ◽  
Normal Light

Low-light image enhancement exhibits an ill-posed nature, as a given image may have many enhanced versions, yet recent studies focus on building a deterministic mapping from input to an enhanced version. In contrast, we propose a lightweight one-path conditional generative adversarial network (cGAN) to learn a one-to-many relation from low-light to normal-light image space, given only sets of low- and normal-light training images without any correspondence. By formulating this ill-posed problem as a modulation code learning task, our network learns to generate a collection of enhanced images from a given input conditioned on various reference images. Therefore our inference model easily adapts to various user preferences, provided with a few favorable photos from each user. Our model achieves competitive visual and quantitative results on par with fully supervised methods on both noisy and clean datasets, while being 6 to 10 times lighter than state-of-the-art generative adversarial networks (GANs) approaches.

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