Sparse Sensing and Machine Learning for Rapid Calibration of Defect Detection with rf Atomic Magnetometers

Machine learning algorithms are utilised to improve the speed of both image acquisition and calibration processes needed for defect detection using radio-frequency atomic magnetometers. Sparse sensing is employed, and an average relative error of ≈5% is observed for a reconstructed image based on 1.25% of the sampled pixels when compared to a raster scan over the target object. Additional algorithms demonstrate the viability of image processing to qualify results and adjust experimental parameters required for calibration, leading to an enhancement of image contrast. This presents a first step in developing a tool for fast, arbitrary defect detection.