"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors

Yosef Gandelsman, Assaf Shocher, Michal Irani; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11026-11035

Abstract


Many seemingly unrelated computer vision tasks can be viewed as a special case of image decomposition into separate layers. For example, image segmentation (separation into foreground and background layers); transparent layer separation (into reflection and transmission layers); Image dehazing (separation into a clear image and a haze map), and more. In this paper we propose a unified framework for unsupervised layer decomposition of a single image, based on coupled "Deep-image-Prior" (DIP) networks. It was shown [Ulyanov et al] that the structure of a single DIP generator network is sufficient to capture the low-level statistics of a single image. We show that coupling multiple such DIPs provides a powerful tool for decomposing images into their basic components, for a wide variety of applications. This capability stems from the fact that the internal statistics of a mixture of layers is more complex than the statistics of each of its individual components. We show the power of this approach for Image-Dehazing, Fg/Bg Segmentation, Watermark-Removal, Transparency Separation in images and video, and more. These capabilities are achieved in a totally unsupervised way, with no training examples other than the input image/video itself.

Related Material


[pdf]
[bibtex]
@InProceedings{Gandelsman_2019_CVPR,
author = {Gandelsman, Yosef and Shocher, Assaf and Irani, Michal},
title = {"Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}