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http://dx.doi.org/10.6109/jkiice.2022.26.4.591

A Broken Image Screening Method based on Histogram Analysis to Improve GAN Algorithm  

Cho, Jin-Hwan (Department of Software Convergence, Dong-Eui University)
Jang, Jongwook (Department of Computer Engineering, Dong-Eui University)
Jang, Si-Woong (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.4, 2022 , pp. 591-597 More about this Journal
Abstract
Recently, many studies have been done on the data augmentation technique as a way to efficiently build datasets. Among them, a representative data augmentation technique is a method of utilizing Generative Adversarial Network (GAN), which generates data similar to real data by competitively learning generators and discriminators. However, when learning GAN, there are cases where a broken pixel image occurs among similar data generated according to the environment and progress, which cannot be used as a dataset and causes an increase in learning time. In this paper, an algorithm was developed to select these damaged images by analyzing the histogram of image data generated during the GAN learning process, and as a result of comparing them with the images generated in the existing GAN, the ratio of the damaged images was reduced by 33.3 times(3,330%).
Keywords
Generative Adversarial Network; Similar Data; Broken Images; Histogram; Screening Algorithm;
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Times Cited By KSCI : 2  (Citation Analysis)
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