Due to its economic advantages, convenience of use, and wide applicability, fluorescence fluctuation-based super-resolution microscopy has rapidly advanced in recent years and has garnered increased attention and application. Compared with other super-resolution imaging techniques, fluorescence fluctuation-based super-resolution microscopy offers lower system costs and is particularly suitable for imaging live cells, demonstrating exceptional performance in observing subcellular structures and monitoring dynamic processes. Specifically, variations in the fluorescence fluctuation characteristics significantly affect the quality of the super-resolution reconstructed images. Therefore, a systematic investigation of image quality under various fluorescence fluctuation conditions is crucial for identifying the most suitable super-resolution imaging approach. These fluorescence fluctuation conditions include parameters such as the number of image-acquisition frames, signal-to-noise ratio, bright-to-dark state probability, and bright-to-dark fluorescence intensity ratio, which directly affect image clarity, the signal-to-noise ratio, and accuracy. Thoroughly examining these conditions, we can effectively select and optimize the super-resolution imaging method that meet specific research requirements and experimental conditions.

We developed a fluorescence fluctuation-based super-resolution comprehensive imaging reconstruction platform using MATLAB. This platform integrates four super-resolution methods, namely, SOFI, MSSR, MUSICAL, and SPARCOM, and can simulate fluorescence fluctuation signals under different conditions while simultaneously applying multiple super-resolution methods to generate datasets. The platform also supports the import and reconstruction of experimental data and presents the reconstruction results clearly and intuitively on the platform interface, thus allowing users to conveniently compare the imaging results of different approaches. A comprehensive image-quality assessment is then conducted on these simulated datasets. This study used four sets of data under different fluorescence fluctuation conditions and quantitatively analyzed the quality of the reconstructed images generated by the four super-resolution algorithms using five evaluation parameters: the resolution-scaled Pearson coefficient (RSP), resolution-scaled error (RSE), relative error of strength (K), signal-to-noise ratio (SNR), and resolution (R). These five parameters were used to determine the image reconstruction consistency, reconstruction error, image reconstruction uniformity, SNR of the reconstructed images, and improvements in the reconstructed image resolution. In addition, to assess the quality of images reconstructed by the super-resolution algorithms more comprehensively and objectively, this study assigned specific weights to these five evaluation parameters and defined a comprehensive evaluation factor (CEF). The weights were determined based on the relative importance of each parameter in the super-resolution imaging technology to ensure the contribution of each parameter was accurately reflected. To facilitate a better comparison of the performances of the four super-resolution algorithms, this study integrated a multilayer perceptron model with a CEF and datasets generated under various fluorescence fluctuation conditions. The model can determine the super-resolution image reconstruction method that best performs under various fluorescence fluctuation conditions by learning and analyzing the performance of different algorithms and outputting an optimal algorithm selection. In short, this model considers different fluorescence fluctuation conditions as inputs and uses a comprehensive evaluation factor of the reconstructed results from various super-resolution algorithms as outputs.

Under the fluorescence fluctuation super-resolution comprehensive imaging reconstruction platform, fluorescence signals under varying fluorescence fluctuation conditions were generated. Super-resolution algorithms were applied to reconstruct the datasets and calculate their CEF values; some simulation results are presented in Table 1. The SPARCOM method demonstrates the best performance in terms of resolution and denoising capability, achieving a spatial resolution of up to 44 nm. However, this method relies heavily on the sparsity of image sequences for super-resolution reconstruction and struggles to reconstruct images accurately when the bright-state probability of the fluorescence fluctuation signal is too high or the bright-dark ratio is too low. The MUSICAL method, which has lower resolution capabilities, offers superior denoising performance but exhibits poor image reconstruction consistency, uniformity, and a longer reconstruction time. The MSSR method has moderate resolution capabilities but exhibits superior image reconstruction consistency and uniformity and can be combined with other super-resolution algorithms to obtain higher-quality super-resolution images. Although the SOFI method has lower resolution and denoising capabilities, it exhibits good image reconstruction consistency and uniformity and exhibits a higher image reconstruction rate. A multi-layer perceptron model was constructed with fluorescence fluctuation characteristics as inputs and the CEF values of different algorithms as outputs. An analysis of the generated and evaluated datasets showed that the constructed model achieves an accuracy of 92.3%, indicating reliable classification and recognition capabilities and enabling intelligent selection of the most suitable super-resolution image reconstruction method under varying fluorescence fluctuation signal conditions.

We developed a comprehensive super-resolution image reconstruction platform using MATLAB, which implements signal generation and super-resolution image reconstruction functions under various fluorescence fluctuation conditions. The performances of multiple super-resolution algorithms across different fluorescence fluctuation scenarios were systematically evaluated. Leveraging of the dataset generated by the software platform enabled us to introduce a multi-layer perceptron model for intelligent algorithm selection. This in turn allowed for accurate classification and identification of the optimal super-resolution technique. This approach enhances research efficiency and assists researchers in selecting the most suitable fluorescence fluctuation method for various subcellular super-resolution imaging studies. The approach can further advance the application of fluorescence fluctuation-based super-resolution imaging techniques for efficient investigation of the ultrafine structures of various biological subcellular organelles.