N. Sundaravalli, Research Scholar & DR. R. Vidyabanu, Assistant Professor
Pg & Research Department Of Computer Science, L.R.G Govt.Arts College For Women, Tirupur – 4, India.
Abstract
At this time, the entire world is facing a new contagious coronavirus disease 2019
(COVID-19) caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV2).
The world health organization (WHO) already declared this infectious disease as a worldwide
pandemic. During this period, the medical X-ray image plays an important role to diagnose COVID-
19 patients effectively. This current research work takes the dataset of COVID-19 affected chest X-
ray and proposes a new method of image de-noising based on using median filter (MF) in the wavelet
domain. In this contemporary exploration, various types of wavelet transform filters are used in
conjunction with median filter, in experimenting with the proposed approach in order to obtain better
results for image de-noising process and, consequently to select the best-suited filter. Wavelet
transforms mathematical tools working on the frequencies of sub-bands split from an image in
different scales. According to this experimental work, the proposed Dual-Tree Complex Wavelet
Transform (DT_CWT) method presents better results than using only wavelet transform methods.
Here, by working on real time images and later adding noise (salt & pepper, Gaussian) to images
and then calculate and comparing the Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE),
Structural Similarity Index (SSIM) value for different images. The present work aims in categorizing
the x-ray images as COVID-19 infected and not infected images to facilitates the effective diagnosis
of the disease and thus can be used to inform future Literature-Based Discovery endeavors.