Voltage imaging using genetically encoded voltage indicators (GEVIs) enables high-speed, population-scale monitoring of neural activity, but it suffers from significant noise due to low photon yield and high frame rates. Effective denoising is essential to recover meaningful signals from such data. In this study, we present a comparative evaluation of three state-of-the-art denoising methods, SUPPORT, DeepCAD-RT, and PMD, on both synthetic and real voltage imaging datasets. Our analysis considers spatial and temporal signal quality, as well as computational efficiency. We find that each method has distinct advantages, and the most suitable choice depends on the specific requirements of the imaging application. SUPPORT is well-suited for tasks requiring spatial detail, PMD offers strong temporal stability and speed, and DeepCAD-RT provides an efficient, balanced alternative. These insights aim to support researchers in selecting and refining denoising tools for real-world neuroscience workflows.