Unsupervised Image Decomposition via Coupled Deep-Image-Priors
Many computer vision tasks aspire to decompose an image into its sole components. In Image segmentation, the image is decomposed into meaningful sub-regions, e.g. foreground and background. In transparency separation, the image is separated into its superimposed reflection and transmission. Another example is the task of image dehazing where the goal is to separate a foggy image into its underlying haze-free image and the obscuring fog layers.
While appear unrelated at first, these tasks can be viewed as a special case of image decomposition into separate layers. For example, as visualized in Figure 1; 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 post, we are going to focus on “Double-DIP”, a unified framework for unsupervised layer decomposition of a single image, based on several “Deep-image-Prior” (DIP) networks.