Anisotropic Diffusion with Deep Learning
Hyun-Tae Choi, Yuna Han, Dahye Kim, Seonghoon Ham, Minji Kim, Yesol Park, Byung‐Woo Hong · Frontiers in artificial intelligence and applications · 2020
We propose a deep learning framework for anisotropic diffusion which is based on a complex algorithm for a single image. Our network can be applied not only to a single image but also to multiple images. Also by blurring the image, the noise in the image is reduced. But the important features of objects remain. To apply anisotropic diffusion to deep learning, we use total variation for our loss function. Also, total variation is used in image denoising pre-process.[1] With this loss, our network makes successful anisotropic diffusion images. In these images, the whole parts are blurred, but edge and important features remain. The effectiveness of the anisotropic diffusion image is shown with the classification task.