Fluorescence Signal Enhancement and Noise Suppression Algorithm for Microscopic Imaging of Plant Living Cells Based on Machine Learning

The purpose of this study is to improve the quality of fluorescence microscopic imaging by integrating and optimizing fluorescence signal enhancement and noise suppression algorithms. Aiming at the weak signal strength of fluorescence signal and its susceptibility to noise interference, this paper proposes a set of algorithm framework based on Machine learning (ML). First of all, in the aspect of algorithm integration strategy, the combination of sequential and parallel processing is adopted to ensure that the complementary role of signal enhancement and noise suppression can be fully exerted. Secondly, the performance of the algorithm is further improved by optimizing parameters, introducing prior knowledge and data enhancement. In the part of simulation experiment design and result analysis, this paper describes the experimental design idea, data set preparation and pretreatment, experimental process and parameter setting, and experimental result display and analysis in detail. The experimental results show that the proposed integrated algorithm has achieved remarkable results in fluorescence signal enhancement and noise suppression. Compared with the traditional methods, the objective evaluation indexes such as PSNR and SSIM are improved, and the subjective visual evaluation also verifies the effectiveness of the algorithm. By integrating and optimizing fluorescence signal enhancement and noise suppression algorithms, the quality of fluorescence microscopic imaging is significantly improved.