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.

Automated summary

The study introduces a deep-learning-based autofocus method for SPRM imaging, using a generative adversarial network (GAN) model trained with thousands of SPRM images to generate focused images from defocused ones. This approach effectively addresses focus inhomogeneity and drift issues in SPRM systems without adding complexity to optical systems, improving the consistency and long-term monitoring capabilities of SPRM studies.