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Free viewpoint television (FTV) is a new concept that aims at giving viewers the flexibility to select a novel viewpoint by employing multiple video streams as the input. Current proposed solutions for FTV include those based on ray-space resampling which demand at least dozens of cameras and large storage and transmission resources for those video streams. Image-based rendering (IBR) methods that rely on dense depth map estimation also face practical difficulties since accurate depth map estimation remains a challenging problem. This paper proposes a framework for FTV based on IBR that relieves the need for an accurate depth map by introducing a hybrid virtual view synthesis method. The framework also includes an intuitive method for virtual view specification in uncalibrated views. We present both simulation and real data experiments to validate the proposed framework and the component algorithms. [ABSTRACT FROM AUTHOR]

Proposes an approach for motion-compensated reduced-order model Kalman filtering for restoration of progressive and interlaced video. How motion-compensation and reduced-order state modeling are achieved; Components of the proposed modeling.

Discusses the applications which motivates superresolution (SR) image and video reconstruction from low resolution (LR) and possibly blurred sources. Description of SR reconstruction methods; Information on video formation; Reference to the a digital camera experiment.

Focuses on robust, object based high resolution image reconstruction from low resolution video. What procedure was used; Indepth look at the projection onto convex sets (POCS) method; Estimation of the object based motion.