Understanding video sequences through super-resolution

Human-centred multimedia applications are a set of activities that human directly interact with multimedia, which consists of different forms. Within all multimedia, video is an ultimate resource, by which people could obtain sensory information. Since limitations on the capacity of imaging devices as well as shooting conditions, we cannot usually acquire high quality video records that desired. This problem could be addressed by super-resolution. We propose a novel scheme in the present paper for super-resolution problem, and make three contributions: (1) on the stage of image registration according to previous approaches, the reference image is picked out through observing or randomly. We utilise a simple but efficient method to select the base image; (2) a median-value image, rather than the average image used previously, is adopted as the initialization for estimate of super-resolution; (3) we adapt the traditional Cross Validation (CV) to a weighted version in the process of learning parameters from input observations. Experiments on synthetic and real data are provided to illustrate the effectiveness of our approach