AFM is a very useful technique to make a topology map of a specimen at a large range of magnifications. However the scanning process can be very time consuming since the probe has to slide across the surface of the sample in order to take the measurements.

The work in this report tries to make this scan time shorter based on cutting edge research in image reconstruction and sample pattern generation.

For reconstruction, there has recently been proposed a way of utilizing the structure of a NN for image reconstruction without training. This method is called DIP.

A two shot adaptive sample pattern is also proposed where at first a crude scan is performed on the specimen. Then the interesting regions are identified, and scanned with the wanted resolution.

The DIP method for reconstruction was deemed infeasible for AFM due to its reconstruction performance being comparable to that of interpolation, while being much more computationally expensive.

The adaptive sample pattern has promising results for the relevant parts of the image. It shows an average speedup of 10 times for a reconstruction with approximately 44 dB PSNR for the relevant parts of the image. This speedup can however vary greatly from image to image but the method will ensure that only the most relevant parts of the image is raster scanned.