Super-resolution radial fluctuations (SRRF) algorithm with better real-time performance

Abstract

Super-resolution radial fluctuations (SRRF) is a type of statistical analysis based on fluorescence fluctuation characteristics to achieve super-resolution imaging. At present, facing the problems of slow reconstruction speed and weak real-time image processing. For dealing with these issues, parallel computing characteristics of FPGA is employed in this paper to achieve the acceleration of the algorithm. Firstly, the reconstruction process of SRRF algorithm process is split into five sub-modules, which are designed depended on the pipeline design idea. Secondly, the top-level module is designed to accomplish the sub-modules connection for executing the SRRF algorithmic process, while the reconstruction is finished by using the high-density image dataset. Finally, the single-frame image processing time is recorded and analysed in FPGA with the mainstream configuration of PC. The experimental results show that, in terms of the single frame image processing time, SRRF in FPGA is improved by 77.95% compared to PC, with real-time image processing capability under 10 ms image sampling period. In addition, the resolution-scaled Pearson coefficient (RSP) and resolution-scaled error (RSE) values of the reconstruction results in FPGA are similar to those of PC.