Super-resolution reconstruction using spatio-temporal filtering

Abstract It has been known for some time that temporal dependence (motion) plays a key role in the super-resolution (SR) reconstruction of a single frame (or sequence of frames). While the impact of global time-invariant translations is relatively well known, the general motion case has not been studied in detail. In this paper, we discuss SR reconstruction for both motion models from a frequency-domain point of view. A noniterative algorithm for SR reconstruction is presented using spatio-temporal filtering. The concepts of motion-compensated windows and sinc interpolation kernels are utilized, resulting in a finite impulse response (FIR) filter realization. In the simulations, we assume a priori knowledge of the motion (optical flow), which is commonly done throughout much of the SR reconstruction literature. The proposed process is localized in nature, and this enables the selective reconstruction of desired parts of a particular frame or sequence of frames.