Space-Angle Super-Resolution for Multi-View Images

Abstract

The limited spatial and angular resolutions in multi-view multimedia applications restrict their visual experience in practical use. In this paper, we first argue the space-angle super-resolution (SASR) problem for irregular arranged multi-view images. It aims to increase the spatial resolution of source views and synthesize arbitrary virtual high resolution (HR) views between them jointly. One feasible solution is to perform super-resolution (SR) and view synthesis (VS) methods separately. However, it cannot fully exploit the intra-relationship between SR and VS tasks. Intuitively, multi-view images can provide more angular references, and higher resolution can provide more high-frequency details. Therefore, we propose a one-stage space-angle super-resolution network called SASRnet, which simultaneously synthesizes real and virtual HR views. Extensive experiments on several benchmarks demonstrate that our proposed method outperforms two-stage methods, meanwhile prove that SR and VS can promote each other. To our knowledge, this work is the first to address the SASR problem for unstructured multi-view images in an end-to-end learning-based manner. CCS CONCEPTS• Computing methodologies → Image-based rendering; Reconstruction; Image processing.

Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution

“…However, obtaining HR images is difficult due to substantial storage costs and bandwidth constraints in reality. Sun et al [37] addresses the space-angle SR problem, but only two adjacent views are used to synthesize virtual view and SR. In general, it is difficult to apply the methods discussed above to the MVISR task directly.…”

“…Some methods focus on multi-view texture [28] or light field images [50] and some other methods [19,24] require high-resolution multiview as a reference, which is not easily available in practice due to high storage costs and bandwidth constraints. The most similar method to ours is SASRnet [37]. It uses two adjacent low-resolution views to generate high-resolution source views and a novel view.…”