@article{MAKHILLAJIT20201996806,
    title = {Comparative Analysis of Thresholding Techniques using DWT for Denoising Image},
    journal = {Asian Journal of Information Technology},
    volume = {19},
    number = {9},
    pages = {193-197},
    year = {2020},
    issn = {1682-3915},
    doi = {ajit.2020.193.197},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2020.193.197},
    author = {Ana,Sugandha and},
    keywords = {Wavelet thresholding,DWT (Discrete Wavelet Transform),denoising,wavelet coefficient,Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR)},
    abstract = {The process of reconstructing the original
image which is corrupted by noise is called de-noising.
Discrete wavelet transform is superior over other
transforms in terms of image denoising as its functions are
localized both in frequency and time domain. The
denoising of image has three major steps decomposition,
thresholding and reconstruction. In this study, the
qualitative and quantitative assessment of the denoised
image is done on the basis of different noise variance and
further analytic result shows that soft threshold is superior
over hard threshold in terms of matrices such as Root
Mean Square Error (RMSE) and Peak Signal-to-Noise
Ratio (PSNR).}
    }