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Many seemingly unrelated computer vision tasks can be viewed as a specialcase of image decomposition into separate layers. For example, imagesegmentation (separation into foreground and background layers); transparentlayer separation (into reflection and transmission layers); Image dehazing(separation into a clear image and a haze map), and more. In this paper wepropose a unified framework for unsupervised layer decomposition of a singleimage, based on coupled "Deep-image-Prior" (DIP) networks. It was shown[Ulyanov et al] that the structure of a single DIP generator network issufficient to capture the low-level statistics of a single image. We show thatcoupling multiple such DIPs provides a powerful tool for decomposing imagesinto their basic components, for a wide variety of applications. Thiscapability stems from the fact that the internal statistics of a mixture oflayers is more complex than the statistics of each of its individualcomponents. We show the power of this approach for Image-Dehazing, Fg/BgSegmentation, Watermark-Removal, Transparency Separation in images and video,and more. These capabilities are achieved in a totally unsupervised way, withno training examples other than the input image/video itself.

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