Publication Type |
Journal Article |
School or College |
College of Engineering |
Department |
Electrical & Computer Engineering |
Creator |
Tasdizen, Tolga |
Title |
Principal components for non-local means image denoising |
Date |
2008 |
Description |
This paper presents an image denoising algorithm that uses principal component analysis (PCA) in conjunction with the non-local means image denoising. Image neighborhood vectors used in the non-local means algorithm are first projected onto a lower-dimensional subspace using PCA. Consequently, neighborhood similarity weights for denoising are computed using distances in this subspace rather than the full space. This modification to the non-local means algorithm results in improved accuracy and computational performance. We present an analysis of the proposed method's accuracy as a function of the dimensionality of the projection subspace and demonstrate that denoising accuracy peaks at a relatively low number of dimensions. |
Type |
Text |
Publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
First Page |
1728 |
Last Page |
1731 |
Language |
eng |
Bibliographic Citation |
Tazdizen, T. (2008). Principal components for non-local means image denoising. Proceedings of the IEEE International Conference on Image Processing, 1728-31. |
Rights Management |
(c) 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Format Medium |
application/pdf |
Format Extent |
303,699 bytes |
Identifier |
ir-main,15215 |
ARK |
ark:/87278/s6cr6bs7 |
Setname |
ir_uspace |
ID |
705012 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6cr6bs7 |