A Single-Shot Autofocus Approach for Surface Plasmon Resonance Microscopy

Surface plasmon resonance microscopy (SPRM) has been widely used as a sensitive imaging platform for chemical and biological analysis. The SPRM system inevitably suffers from focus inhomogeneity and drifts, especially in long-term recordings, leading to distorted images and inaccurate quantification. Traditional focus correction approaches require additional optical parts to detect and adjust focal conditions. Herein, we propose a deep-learning-based image processing method to gain autofocused SPRM images, without increasing the complexity of the optical systems. We trained a generative adversarial network (GAN) model with thousands of SPRM images of nanoparticles acquired at different focal distances. The trained model was able to directly generate focused SPRM images from single-shot defocused images, with no prior knowledge of the focus conditions during recording. Experiments using Au nanoparticles show that this method is effective in both static and time-lapse monitoring. The proposed autofocus technique thus provides an approach for improving the consistency among SPRM studies and for long-term monitoring.