Voltage imaging enables high resolution recordings of neuronal activity but suffers from low signal-to-noise ratios (SNR), primarily due to photon shot noise. Traditional denoising methods like VST-GAT and Penalized Matrix Decomposition (PMD) offer effective noise reduction but often trade off temporal and spatial resolution. Recently, deep learning-based denoising methods, such as CellMincer, have emerged as promising alternatives due to their ability to learn complex signal models without requiring clean training data. This paper compares the performance of traditional and deep learning methods for denoising voltage imaging data using both synthetic and in vivo datasets. Metrics such as SNR, PSNR, and tSNR were used to evaluate performance. The results show that CellMincer outperforms traditional methods on synthetic data and performs competitively on real in vivo recordings, suggesting the viability of self-supervised deep learning for voltage imaging denoising. PMD remains a strong baseline with robust performance across datasets. This comparative study highlights both the potential and current limitations of deep learning approaches and suggests directions for future improvement.