Sum frequency generation-compressive sensing microscope

Abstract

A new sum frequency generation imaging microscope using a novel sampling theory, compressive sensing (CS), has been developed for surface studies. CS differentiates itself from the conventional sampling methods by collecting fewer measurements than the traditional methods to reconstruct a high quality image. Pseudorandom patterns were applied to a light modulator and reflected the sum frequency (SF) signal generated from the sample into a photomultiplier tube detector. The image of the sample was reconstructed using sparsity preserving algorithms from the SF signal. The influences of the number of CS testing patterns applied and the number of SF pulses acquired for each pattern on the quality of the images was investigated and a comparison of the image quality with the traditional raster scan was made at varying resolutions for a gold patterned Si surface. Our results demonstrate the CS technique achieved 16 times the pixel density beyond the resolution where the raster scan strategy lost its ability to image the sample due to the dilution of the SF signal below the detection limit of the detector.