Resizing and cleaning of histopathological images using generative adversarial networks

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.