A Computationally Efficient Super-Resolution Algorithm for Video Processing Using Partition Filters

We propose a computationally efficient super-resolution (SR) algorithm to produce high-resolution video from low-resolution (LR) video using partition-based weighted sum (PWS) filters. First, subpixel motion parameters are estimated from the LR video frames. These are used to position the observed LR pixels into a high-resolution (HR) grid. Finally, PWS filters are employed to simultaneously perform nonuniform interpolation (to fully populate the HR grid) and perform deconvolution of the system point spread function. The PWS filters operate with a moving window. At each window location, the output is formed using a weighted sum of the present pixels within the window. The weights are selected from a filter bank based on the configuration of missing pixels in the window and the intensity structure of the present pixels. We present an algorithm for applying the PWS SR filters to video that is computationally efficient and suitable for parallel implementation. A number of experimental results are presented to demonstrate the efficacy of the proposed algorithm in comparison to several previously published methods. A detailed computational analysis of the partition-based SR filters is also presented