Medizadeh_ Fatemi.pdf (2.56 MB)
Download fileIterative based image and video denoising by fractional block matching and transform domain filtering
preprint
posted on 2021-01-25, 14:48 authored by Amir Mehdizadeh Hemat Abadi, Mohammad Reza Hosseiny FatemiMohammad Reza Hosseiny FatemiThis paper presents an iterative algorithm for image and video denoising which is based
on fractional block-matching and transform domain filtering. We propose fractional motion
estimation technique to find the most accurate similar blocks for each block of an image
which improves sparsity enabling effective image denoising. By taking the advantage of
blocks similarity and wavelet transform domain filtering along with weighted average
function (WAF) in an iterative based manner, we achieve a higher level of sparsity and a
better exploiting of blocks similarity redundancies of noisy images that increase the chance of
preserving details and edges in the restored image. Since our algorithm is iterative, we can
tradeoff between image denoising degree and computational complexity. In addition, we
develop a video denoising algorithm based on the proposed image denoising algorithm. The
simulation results of images and videos contaminated by additive white Gaussian noise
demonstrate that our algorithm substantially achieves better denoising performance compared
with previously published algorithms in terms of subjective and objective measures.
History
Email Address of Submitting Author
fatemi74@gmail.comSubmitting Author's Institution
Imam Reza International UniversitySubmitting Author's Country
- Iran