A compressive sensing-based pixel sharing algorithm for high-speed atomic force microscopy

Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). The undersampled AFM images typically can be faithfully reconstructed with signal recovery techniques such as inpainting or algorithms from compressive sensing. In this paper, we consider the case when the dynamic processes of the sample occur in a small proportion of the entire scanning area of AFM while the background is relatively static and only slowly changing. In this setting, two consecutive video frames, termed the reference frame and the target frame, share a significant amount of static regions in common. Based on the measurements, we use greedy algorithms to select measured pixels in the reference frame that are likely to be from the common static regions and share them to the target frame. The target frame can then be reconstructed from both the original and shared pixels, yielding a more accurate reconstruction. This algorithm is then extended to the more realistic problem of multiple frames. Through simulation, we demonstrate that the proposed algorithm can achieve better overall video reconstruction quality compared to the frame-to-frame based single image reconstruction.