Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain

Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain
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© 2022 by IJETT Journal
Volume-70 Issue-11
Year of Publication : 2022
Authors : Swati Rane, Lakshmappa Ragha, Siddalingappagouda Biradar, Vaibhav Pandit
DOI : 10.14445/22315381/IJETT-V70I11P203

How to Cite?

Swati Rane, Lakshmappa Ragha, Siddalingappagouda Biradar, Vaibhav Pandit, "Image Denoising using Adaptive Patch Clustering with Suboptimal Wiener Filter in PCA Domain," International Journal of Engineering Trends and Technology, vol. 70, no. 11, pp. 19-27, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I11P203

Abstract
The degradation in the visual quality of images is often seen due to various noises added inevitably at the time of image acquisition. Its restoration has thus become a fundamental and significant problem in image processing. Many attempts have been made in the recent past to efficiently denoise such images. But, the best possible solution to this problem is still an open research problem. This paper validates the effectiveness of one such popular image denoising approach, where an adaptive image patch clustering is followed by the suboptimal Wiener filter operation in the Principal Component Analysis (PCA) domain. The experimentation is conducted on grayscale images corrupted by four different noise types: speckle, salt & pepper, Gaussian, and Poisson. The efficiency of image denoising is quantified in terms of various famous image quality metrics. The comprehensive performance analysis of the denoising approach against the four noise models underlies its suitability for various applications. It certainly gives the new researchers a direction for selecting the image-denoising method.

Keywords
Adaptive clustering, Image denoising, Principal component analysis, Wiener filter.

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