NDLI: Image denoising using 2D orthogonal locality preserving discriminant projection

Content Provider	IET Digital Library
Author	Shikkenawis, Gitam Mitra, Suman K.
Abstract	Image denoising using a combination of non-local self-similarity and transformed domain techniques has become popular in past few years. Instead of working on independent pixels, patches extracted from the noisy image are grouped together based on structural similarity and noise elimination is performed in transformed domain. Orthogonal locality preserving projection and its variant that processes the images directly in matrix format have been used for image denoising recently. Locality preserving nature of these techniques takes care of similarity within image patches while learning the basis, hence reducing the task of grouping patches explicitly. Non-local self-similarity based image denoising approaches perform patch grouping based on structural similarity. Discriminant information, if considered can play pivotal role in achieving superior clustering of data and thereby is expected to enhance the quality of denoising. With this aim in mind, two-dimensional (2D) orthogonal locality preserving discriminant projection is formulated in this study. While learning the basis, along with the similarity, proposed approach also takes into account dissimilarity between patches. A global basis thus learnt from the noisy image is used for denoising and comparable denoising performance is shown relative to the state-of-the-art methods.
Starting Page	552
Ending Page	560
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0436
Journal	IET Image Processing
Publisher Date	2019-11-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	2D Orthogonal Locality Comparable Denoising Performance Computer Vision And Image Processing Technique Denoising Denoising Performance Discriminant Projection Domain Technique Image Denoising Image Patches Knowledge Engineering Technique Learning in AI Locality Preserving Nature Noisy Image Nonlocal Self-similarity Based Image Denoising Approach Optical, Image And Video Signal Processing Orthogonal Locality Preserving Projection Pattern Clustering Structural Similarity Transform Transformed Domain Two-dimensional Orthogonal Locality
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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