|Table of Contents|

Wild Image Enhancement with Conditional Generative Adversarial Network(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年03期
Page:
88-95
Research Field:
·全国机器学习会议论文专栏·
Publishing date:

Info

Title:
Wild Image Enhancement with Conditional Generative Adversarial Network
Author(s):
Jia Yufu1; Hu Shenghong2; Liu Wenping1; Wang Chao2; Xiang Shucheng2
(1.Information Management and Statistics School,Hubei University of Economics,Wuhan 430205,China)(2.Information and Communication Engineering School,Hubei University of Economics,Wuhan 430205,China)
Keywords:
image enhancement; generative adversarial network; deep learning; image quality
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2019.03.012
Abstract:
Wild image enhancement technology is a hotspot in the field of computer vision. To overcome the defects on complexity of calculation and manual setting parameters of the conventional image enhancement methods,a novel image enhancement method with conditional generative adversarial network(E-CGAN)has been proposed. The generative neural network and the discriminant neural network are constructed respectively,where the generative model is used to generate the final images and the discriminant model is employed to construct the confrontation loss function in the training stage,so as to optimize the parameters of the two models. In the structure of the generative model,a successive-multiple skip connection method constrained by L1 error function is proposed,which speeds up the training speed of the network and improves the accuracy of the generative model. Two implementations on image sharpening and colorization have been implemented to evaluate the effectiveness of the proposed method,the experimental results show that E-CGAN can effectively highlight the characteristics of the image,and better quality promotion achieves up to 9% and 5% both on PSNR and SSIM index.

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Last Update: 2019-09-30