Medical image denoising using wavelet transform and singular value decomposition

Abstract

Filtering noise to recreate a high-quality image in medical image processing is an important task. During acquisition, transmission, and retrieval from storage devices, generally images are getting corrupted. So, for further analysis images must get denoised. The noises can be categorised into different types based on their nature and origin. Researchers are still looking for the effective denoising technique. Wavelet Transform (WT) is an effective transform method for denoising. Similarly Singular Value Decomposition (SVD) is also an important tool for denoising. Combining WT with SVD results in further reduction of noise. This paper proposes use of WT along with SVD for medical image denoising. Performance of image denoising is evaluated on the basis of Signal to Noise Ratio (SNR) and Peak Signal-Noise Ratio (PSNR). In the proposed approach, experimental results of WT-SVD combination gives better SNR and PSNR values than WT and SVD, if used independently.