Order filters in superresolution image reconstruction

In this paper, we propose the use of order filters in the iterative process of super-resolution reconstruction. At each iteration, order statistic filters are used to filter and fuse the error images. The signal dependent L -filter structure adjusts its coefficients to achieve edge preservation as well as maximum noise suppression in homogeneous regions. Depending on the amount of variance of the image pixels in different directional masks, the filter switches to use the orientation, which is most likely to follow the image edges. This procedure allows for the incorporation of a directional prior across the iterations. The introduction of a spatial filtering stage into the iterative process of super-resolution attempts to increase the robustness towards motion error and image outliers. Experimental results show the improvement obtained on sequences of noisy text images when motion is exactly known, and when a random motion error is introduced to simulate the real life situation of inaccurate motion estimation.