The time-stamped camera Tpx3Cam is a cutting-edge tool for exploring atomic and molecular dynamics, enabling the detection of photons, electrons, and ions in three dimensions with an impressive time resolution of up to 1.6 ns. Despite its advantages, Tpx3Cam faces inherent challenges, such as the cluster effect. This effect compromises both the temporal and spatial resolution of data acquisition while significantly increasing data capacity, thereby posing obstacles for subsequent data processing. To counter this, a method, known as the centroiding algorithm, is crucial to mitigate the cluster effect's impact, enhance Tpx3Cam's imaging resolution, and reduce data capacity. The current centroiding algorithm efficiently eliminates unnecessary derived signals within clusters and accurately locates their centers by analyzing their distributions, achieving subpixel super-resolution in position. However, existing centroiding algorithms are limited to handling low counting rates, specifically dealing with isolated clusters, lacking the capability to distinguish connected clusters in position. Under high counting rates, closely situated clusters could emerge within a short time. Consequently, traditional centroiding algorithms is inadequate for declustering in such scenarios.A new centroiding algorithm has been developed to address the cluster effect encountered during high counting rate imaging processes. Based on the existing centroiding algorithm, this new method significantly enhances the capability to distinguish clusters in time. It accurately identifies each independent cluster within extensive datasets, effectively declustering them. It results in a data capacity reduction by approximately one order of magnitude, while achieving subpixel super-resolution of the cluster center location. A position resolution of about 0.1 pixel could be achieved with the application of this new algorithm for each signal. Additionally, instead of employing Gaussian fitting, we utilize the weighted average method to determine cluster centers. This choice is supported by its equivalence to Gaussian fitting, as proven in the article. Notably, the weighted average method exhibits higher efficiency compared to Gaussian fitting. It's approximately 10³ times faster in locating cluster centers in calculations.To validate the impact of the centroiding algorithm on Tpx3Cam imaging in practical experiments, we conducted simulations using SIMION to replicate the imaging process of electrons and ions in a typical Velocity Map Imaging(VMI) system. By simulating the ionization of ns state electrons and the Coulomb explosions of N₂ from the (1,1) channel in VMI experiments, we observed significant improvements. The centroiding algorithm reduced the Full Width at Half Maximum (FWHM) of the electron's position distribution by 30%, thereby enhancing momentum resolution by 30% along the detector plane. Moreover, it reduced the FWHM of the time-of-flight (ToF) distribution of N⁺ from Coulomb explosions by 80%, leading to an 80% enhancement in time resolution. Variations might occur with alterations in the initial conditions of electrons and ions, the overall improvements in position and time resolution remain consistent. Consequently, the centroiding algorithm demonstrates its efficacy in enhancing momentum resolution in practical electron and ion detection experiments. Furthermore, conducting covariance analysis on the ions' radius distribution resulting from the Coulomb explosion of CO with background gas interference, after the implementation of the centroiding algorithm, successfully revealed the correlation between C⁺ and O⁺. This algorithm effectively mitigates count fluctuation interference induced by the cluster effect and remains unaffected by background impurities. Finally, the impact of count rate on the centroiding algorithm is addressed. Excessively high count rates pose a risk of data loss when employing the centroiding algorithm. We are actively addressing this concern and working towards resolving this flaw in the algorithm, aiming for a solution in the near future.