Real-time data processing in colorimetry camera-based single-molecule localization microscopy via CPU-GPU-FPGA heterogeneous computation

Because conventional low-light cameras used in single-molecule localization microscopy (SMLM) do not have the ability to distinguish colors, it is often necessary to employ a dedicated optical system and/or a complicated image analysis procedure to realize multi-color SMLM. Recently, researchers explored the potential of a new kind of low-light camera called colorimetry camera as an alternative detector in multi-color SMLM, and achieved two-color SMLM under a simple optical system, with a comparable cross-talk to the best reported values. However, extracting images from all color channels is a necessary but lengthy process in colorimetry camera-based SMLM (called CC-STORM), because this process requires the sequential traversal of a massive number of pixels. By taking advantage of the parallelism and pipeline characteristics of FPGA, in this paper, we report an updated multi-color SMLM method called HCC-STORM, which integrated the data processing tasks in CC-STORM into a home-built CPU-GPU-FPGA heterogeneous computing platform. We show that, without scarifying the original performance of CC-STORM, the execution speed of HCC-STORM was increased by approximately three times. Actually, in HCC-STORM, the total data processing time for each raw image with 1024 × 1024 pixels was 26.9 ms. This improvement enabled real-time data processing for a field of view of 1024 × 1024 pixels and an exposure time of 30 ms (a typical exposure time in CC-STORM). Furthermore, to reduce the difficulty of deploying algorithms into the heterogeneous computing platform, we also report the necessary interfaces for four commonly used high-level programming languages, including C/C++, Python, Java, and Matlab. This study not only pushes forward the mature of CC-STORM, but also presents a powerful computing platform for tasks with heavy computation load.