Surpassing Traditional Image-Colorization Problems with Conditional Generative Adversarial Networks

2019 ◽  
pp. 37-48
Author(s):  
Vishnu Teja Yalakuntla ◽  
Rahul Kanojia ◽  
Kushagra Chauhan ◽  
Rohit Gurnani ◽  
Mukesh A. Zaveri
Keyword(s):  
Generative Adversarial Networks ◽  
Image Colorization ◽  
Adversarial Networks ◽  
Traditional 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 Image Colorization Progress: A Review of Deep Learning Techniques for Automation of Colorization

2021 ◽  
Vol 10 (4) ◽  
pp. 2908-2915
Keyword(s):  
Neural Networks ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Image Colorization ◽  
Black And White ◽  
The Past ◽  
Adversarial Networks ◽  
Learning Techniques ◽  
Made In

Image colorization is the process of taking an input gray- scale (black and white) image and then producing an output colorized image that represents the semantic color tones of the input. Since the past few years, the process of automatic image colorization has been of significant interest and a lot of progress has been made in the field by various researchers. Image colorization finds its application in many domains including medical imaging, restoration of historical documents, etc. There have been different approaches to solve this problem using Convolutional Neural Networks as well as Generative Adversarial Networks. These colorization networks are not only based on different architectures but also are tested on varied data sets. This paper aims to cover some of these proposed approaches through different techniques. The results between the generative models and traditional deep neural networks are compared along with presenting the current limitations in those. The paper proposes a summarized view of past and current advances in the field of image colorization contributed by different authors and researchers.

Sign in / Sign up

Export Citation Format

Share Document