Fast super-resolution using an adaptive Wiener filter with robustness to local motion
We present a new adaptive Wiener filter (AWF) super-resolution (SR) algorithm that employs a global background motion model but is also robust to limited local motion. The AWF relies on registration to populate a common high resolution (HR) grid with samples from several frames. A weighted sum of local samples is then used to perform nonuniform interpolation and image restoration simultaneously. To achieve accurate subpixel registration, we employ a global background motion model with relatively few parameters that can be estimated accurately. However, local motion may be present that includes moving objects, motion parallax, or other deviations from the background motion model. In our proposed robust approach, pixels from frames other than the reference that are inconsistent with the background motion model are detected and excluded from populating the HR grid. Here we propose and compare several local motion detection algorithms. We also propose a modified multiscale background registration method that incorporates pixel selection at each scale to minimize the impact of local motion. We demonstrate the efficacy of the new robust SR methods using several datasets, including airborne infrared data with moving vehicles and a ground resolution pattern for objective resolution analysis.