Time‐Efficient Object Recognition in Quantum Ghost Imaging. Issue 2 (25th December 2022) - Moodley, Chané; Ruget, Alice; Leach, Jonathan; Forbes, Andrew
Abstract: Acquiring information at the fastest possible rate is often desirable, particularly in quantum ghost imaging which suffers from slow reconstruction speeds. Many computationally intense deep‐learning methods have been implemented in an effort to speed up image acquisition times by retrieving image information. Often over‐looked, machine learning methods can offer the same, if not better, speed up in image acquisition time by an object recognition process. Four machine learning algorithms are implemented and trained on a uniquely generated, noised, and blurred dataset of numerical digits 1 through 9. Of the tested recognition algorithms, logistic regression shows a 10× speed up in image acquisition time with a 99% prediction accuracy. Additionally, this reduction in acquisition time is achieved without any image denoising or enhancement prior to recognition, thereby reducing training and implementation time, as well as the computational intensity of the approach. This method can be implemented in real‐time, requiring only 1/10 th of the measurements needed for a general solution, making it ideal for quantum imaging and recognition of light sensitive structure. Abstract : Acquiring information at the fastest possible rate is often desirable, particularly in quantum imaging which suffers from slow reconstruction speeds. Machine learning methods offer significant reduction in image acquisition times. Here, logistic regression shows a 10× speed up with a 99% prediction accuracy. This algorithm can be implemented in real‐time making it ideal for the recognition of light sensitive structures.