Ophthalmologists are paying more attention to optical coherence tomography (OCT), a macular screening tool for identifying early-stage retinal diseases. It is a non-invasive process that uses the interferometric principle to examine the innermost layers of retina. OCT causes a coarse speckle pattern while backscattering the light, which reduces the clarity of the image. The literature has implemented a variety of denoising techniques to eliminate speckle noise. In this study, denoising methods such the Gaussian filter, anisotropic diffusion, nonlocal means (NLM), BM3D, K-SVD, and WNNM are compared. Images from the Duke Dataset are used to analyze performance. Peak Signal To Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Correlation (NK), Normalized Absolute Error (NAE), and Average Difference are used to compare the quantitative results (AD). As a result, WNNM offers images of greater quality with significant SSIM and PSNR values.