Image denoising is a vital process in image pre-processing, particularly for applications focused on image-based objectives. This process, which occurs during image acquisition and transmission, is crucial for enhancing image quality to facilitate subsequent analysis by medical image processing algorithms. Given its importance in improving medical images, image denoising has become a prominent research focus. This paper explores the latest advancements in denoising techniques specifically tailored for magnetic resonance imaging (MRI), offering a detailed examination of their publication details, underlying methodologies, strengths, limitations, and accuracy metrics, including peak signal-to-noise ratio (PSNR). The study provides a thorough review of contemporary denoising strategies proposed by researchers such as Taherkhani et al., Zhang et al., Yuan et al., and Chen et al., among others, presenting an in-depth survey of current denoising algorithms. Understanding these methods is critical for selecting robust denoising techniques capable of mitigating artifacts like salt-and-pepper noise, which is essential for effective medical image segmentation. This paper aims to provide valuable insights into denoising methodologies, thereby advancing MRI image processing in the medical domain.