Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Image denoising is a fundamental challenge in
digital image processing, crucial for enhancing visual qual-
ity and ensuring reliable analysis in fields such as medical
imaging, surveillance, and satellite image interpretation. This
paper proposes a hybrid denoising framework combining the
Discrete Wavelet Transform (DWT) with the Non-Local Means
(NLM) filter to efficiently remove Gaussian noise while preserving
edges and fine image structures. The DWT decomposes the
image into multiscale frequency components, allowing targeted
denoising of high-frequency bands, while the NLM exploits patch
redundancy to smooth noise with minimal blurring. Additionally,
Canny edge detection is incorporated to reinforce edge structures
during reconstruction. Experimental results on standard test
images demonstrate that the proposed method achieves superior
performance in terms of Peak Signal-to-Noise Ratio (PSNR) and
Structural Similarity Index (SSIM), outperforming conventional
filtering techniques.
"Noise Removal in Images Using Non-Local Means Filter and Discrete Wavelet Transform", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b746-b751, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505185.pdf
Downloads:
000476
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator