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

Understand

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.