novel framework for single/multi-frame super-resolution using sequential monte carlo method
We propose a novel super-resolution (SR) framework based on a sequential Monte Carlo (SMC) method, which is capable of robust optimization, for solving the inverse problem of degradation processes of imagery and sampling. The SR image is estimated from a set of multiple hypotheses, which are sequentially reorganized by evaluating their consistency with the input image. The concepts of norm regularization and motion registration in single/multi-frame SR are mapped into stochastic processes of an SMC's proposal distribution. The experiments showed that our framework is capable of seamlessly restoring both static and moving regions of degraded pictures.
super-resolution sequential monte carlo method regularization registration image restoration total variation norm