Resizing and cleaning of histopathological images using generative adversarial networks
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Bilinear and Bicubic interpolation techniques are frequently used to increase image resolution. These techniques with data modeling approach are replaced by intelligent systems that can learn automatically from data. SRGAN is a modern Generative Adversarial Network developed as an alternative to classical interpolation techniques. His ability to produce images in super resolution has attracted the attention of many researchers. In this study, noise elimination performance of super resolution generative adversarial network (SRGAN) with image magnification was investigated. The results of the noise cleaning were compared with the classical approaches (mean, median, adaptive filters). SSIM, PSNR and FFT_MSE metrics were evaluated in experimental studies using images in the data set Camelyon17. When the results were evaluated, it was observed that SRGAN was superior to the classical approaches not only in increasing the resolution but also in the noise cleaning area. (C) 2019 Published by Elsevier B.V.
Açıklama
Anahtar Kelimeler
SRGAN, Noise cleaning, Image resizing, Bicubic, Camelyon17
Kaynak
Physica A-Statistical Mechanics and Its Applications
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
554
