Computer Vision News - May 2019

Every month, Computer Vision News reviews a research paper from our field. This month we have chosen "Double-DIP": Unsupervised Image Decomposition via Coupled Deep- Image-Priors . We are indebted to the authors ( Yossi Gandelsman, Assaf Shocher and Michal Irani ), for allowing us to use their images. The paper is found here . Many apparently unrelated computer vision tasks can be thought of and dealt with as special cases of decomposition into separate layers. To name just a couple of prominent examples: • Image segmentation -- which can be defined as decomposition into areas belonging to a background layer and areas belonging to a foreground layer. • Image dehazing -- which can be defined as decomposition into a clear, clean image and the dehazing map layer. The authors propose a unified framework for unsupervised layer decomposition of a single image, based on Deep-image-Prior (DIP) networks. Deep-image-Prior (DIP) networks, introduced at CVPR 2018 , are a type of generative network that learns the low level statistics of a single image -- is trained on a single image. In the article, the authors show how stringing together several DIP networks provides a powerful tool for decomposing images into their basic elements -- for a wide variety of tasks. The authors believe this versatile applicability derives from the fact the internal statistics of a mixture of layers is more robust and has better representation capabilities than each layer separately. The authors show the applicability of their approach to a variety of computer vision tasks, including watermark-removal, Fg / Bg segmentation, image dehazing and transparency decomposition in video images, among others. All of these capabilities are achieved when the network is trained on a single image with no additional data provided. A unified framework for image decomposition -- below are illustrations of the article’s approach in action. Three different tasks redefined as decomposition of the original image viewed as a mixture of simpler basic layers. This approach of image decomposition into a number of basic layers -- provides a unified framework for dealing with a wide number of apparently disparate and unrelated computer vision tasks. 4 Research by Assaf Spanier Computer Vision News Research