Multi-modal Medical Image Denoising using Wavelet Transform and Principal Component Analysis

Medical image denoising performs a crucial role in image processing since noise can considerably deteriorate image quality. In medical imaging, Gaussian and Salt & Pepper noise are commonly encountered during image acquisition, transfer, and storage. To enhance the quality of medical images, researchers are continuously investigating effective denoising techniques that can produce high-quality images, characterized by low Root Mean Square Error (RMSE) values and high Peak Signal-to-Noise Ratio (PSNR). Two powerful denoising methods, Wavelet Transform (WT) and Principal Component Analysis (PCA), have been widely utilized in denoising medical images. In this research, experiments are conducted on X-ray, MRI and CT images, by adding Gaussian and Salt & Pepper noise. The denoising performance of WT and PCA was compared based on their RMSE and PSNR results.