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.

The development of convolutional neural networks for deep learning has significantly contributed to image classification and segmentation areas. For high performance in supervised image segmentation, we need many ground-truth data. However, high costs are required to make these data, so unsupervised manners are actively being studied. The Mumford–Shah and Chan–Vese models are well-known unsupervised image segmentation models. However, the Mumford–Shah model and the Chan–Vese model cannot separate the foreground and background of the image because they are based on pixel intensities. In this paper, we propose a weakly supervised model for image segmentation based on the segmentation models (Mumford–Shah model and Chan–Vese model) and classification. The segmentation model (i.e., Mumford–Shah model or Chan–Vese model) is to find a base image mask for classification, and the classification network uses the mask from the segmentation models. With the classifcation network, the output mask of the segmentation model changes in the direction of increasing the performance of the classification network. In addition, the mask can distinguish the foreground and background of images naturally. Our experiment shows that our segmentation model, integrated with a classifier, can segment the input image to the foreground and the background only with the image’s class label, which is the image-level label.

Humans can recognize objects well even if they only show the shape of objects or an object is composed of several components. But, most of the classifiers in the deep learning framework are trained through original images without removing complex elements inside the object. And also, they do not remove things other than the object to be classified. So the classifiers are not as effective as the human classification of objects because they are trained with the original image which has many objects that the classifier does not want to classify. In this respect, we found out which pre-processing can improve the performance of the classifier the most by comparing the results of using data through other pre-processing. In this paper, we try to limit the amount of information in the object to a minimum. To restrict the information, we use anisotropic diffusion and isotropic diffusion, which are used for removing the noise in the images. By using the anisotropic diffusion and the isotropic diffusion for the pre-processing, only shapes of objects were passed to the classifier. With these diffusion processes, we can get similar classification accuracy compared to when using the original image, and we found out that although the original images are diffused too much, the classifier can classify the objects centered on discriminative parts of the objects.

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