Adaptive Compressive Sensing Imaging in AFM Based on Target Block Detection

Atomic force microscopy (AFM) is essential for studying the surface properties of samples at the micro- and nanoscales. Traditional AFM scanning methods are time-consuming, particularly for obtaining high-resolution images. Compressive sensing (CS) has been utilized for fast AFM imaging. However, as the size and resolution requirements of the images increase, the measurement matrix for compressive sensing also becomes larger. Block compressive sensing (BCS) divides the image into blocks and reconstructs them with a small measurement matrix, but it is difficult to balance the imaging quality between regions. Therefore, we propose an innovative adaptive CS-AFM imaging scheme. A low-resolution image is obtained through fast scanning, and a high-resolution image is generated using bicubic interpolation. The Otsu and eight-connectivity methods detect the location of the target blocks, while the GRNN model adapts the sampling rate for it. A supplementary scan is performed on the target block, followed by reconstruction using the TVAL3 algorithm. Finally, the target region is replaced with the reconstructed high-quality target blocks. Compared to other schemes, the results demonstrate that our method excels in achieving fast, high-quality, and high-resolution imaging.