Super-resolved free-viewpoint image synthesis combined with sparse-representation-based super-resolution

We consider super-resolved free-viewpoint image synthesis (SR-FVS), where a high-resolution (HR) image that would be observed from a virtual viewpoint is synthesized from a set of low-resolution multi-view images. In previous studies, methods for SR-FVS were proposed on the basis of reconstruction-based super-resolution (RB-SR). RB-SR uses multiple images to synthesize an HR image and thereby can naturally be applied to SR-FVS, where multi-view images are given as the input. However, the quality of the synthesized image depends on observation conditions such as the depth of the target scene, so sometimes the quality of SR-FVS can degrade severely. To mitigate such degradation, we propose integrating learning-based super-resolution (LB-SR), which uses knowledge learned from massive natural images, into the SR-FVS process. In this paper, we adopt sparse coding super-resolution (ScSR) as a LB-SR method and combine ScSR with an existing SR-FVS method.