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.

本文提出了一种基于耦合的深度图像先验（DIP）网络的单图像无监督层分解方法，可以用于图像分割、透明度分离、去水印等计算机视觉任务。