Low-Rank Constrained Super-Resolution for Mixed-Resolution Multiview Video

Multiview video allows for simultaneously presenting dynamic imaging from multiple viewpoints, enabling a broad range of immersive applications. This paper proposes a novel super-resolution (SR) approach to mixed-resolution (MR) multiview video, whereby the low-resolution (LR) videos produced by MR camera setups are up-sampled based on the neighboring HR videos. Our solution analyzes the statistical correlation of different resolutions between multiple views, and introduces a low-rank prior based SR optimization framework using local linear embedding and weighted nuclear norm minimization. The target HR patch is reconstructed by learning texture details from the neighboring HR camera views using local linear embedding. A low-rank constrained patch optimization solution is introduced to effectively restrain visual artifacts and the ADMM framework is used to solve the resulting optimization problem. Comprehensive experiments including objective and subjective test metrics demonstrate that the proposed method outperforms the state-of-the-art SR methods for MR multiview video.