Microscopic Particle Localization Under Low-Light Conditions

Microscopic localization of particles under low-light conditions is a challenging task in microscopy, e.g. in biological physics and quantum photonics. While there has been considerable research on developing novel computational techniques, relatively less attention has been paid to integrated computational-experimental approaches leveraging hardware binning. Hardware binning allows one to judiciously sacrifice resolution, which is often excessive in locating objects of several pixels wide, to enhance the signal-to-noise ratio. Our research questions the default choice of single pixel measurements and investigates potential gains by combining multiple frames with differently hardware-binned images. Given the recent success in deep learning microscopy, we also investigate the use of deep residual learning in computing the convolution of the image with a suitable kernel for localization.