Serafim Korovin

This study quantifies spatio-temporal image degradation from motion blur in fluorescence microscopy using SSNR and simulations, showing resolution loss up to 40% for 90-degree motion direction changes.

This paper develops a deep learning model to automate microtrace recognition in forensic microscopy, reducing manual annotation needs by 65% through combined pretraining strategies.

CLEAR-IT improves cell phenotyping in tumor microenvironments by using contrastive learning, outperforming existing methods across imaging modalities and requiring fewer labels.

This paper introduces a spline-based method for depth-dependent PSF calibration in localization microscopy, improving PSF accuracy and localization precision.