COVID-19 and MRI Image Denoising Using Wavelet Transform and Basic Filtering
S. Kavitha, H. Hannah Inbarani · 2021
Eliminating noise from digital image is considered as an essential problem in image processing, for example in medical images as the noise present in the image will degrade the image quality which cannot be further classified with high accuracy. Hence, it is required to provide the images without any noises for better classification. Since the selection of denoising method to generate the quality image depends on the type of noise present in the image, it is suggested to find the best method to eliminate the noise as far as possible by preserving the edges. This paper presents the various denoising techniques such as Bayes Shrink and VisuShrink wavelet transform, non-local mean filter and median filter to remove the noises added through Gaussian, Speckle and salt and pepper noise in the COVID 19 chest x-ray images and the MRI images. The experiment shows that wavelet transform method performance is good for removing Gaussian noise and median filter performance is good to remove speckle and salt and pepper noise. The results are measured through visually and through the quality measure using Peak Signal to Noise Ratio (PSNR).