Adaptive principal components and image denoising (2003)
by
D. Darian Muresan
| Venue: | in Proc. Int. Conf. Image Processing |
| Citations: | 18 - 1 self |
BibTeX
@INPROCEEDINGS{Muresan03adaptiveprincipal,
author = {D. Darian Muresan},
title = {Adaptive principal components and image denoising},
booktitle = {in Proc. Int. Conf. Image Processing},
year = {2003},
pages = {101--104}
}
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Abstract
This paper presents a novel approach to image denoising using adaptive principal components. Our assumptions are that the image is corrupted by additive white Gaussian noise. The new denoising technique performs well in terms of image visual fidelity, and in terms of PSNR values, the new technique compares very well against some of the most recently published denoising algorithms. 1.
Citations
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| 132 | Spatially adaptive wavelet thresholding with context modeling for image denoising - Chang, Yu, et al. - 2000 |
| 33 | On Denoising and Best Signal Representation - Krim, Tucker, et al. - 1999 |
| 23 | Spatially adaptive image denoising under overcomplete expansion - Li, Orchard - 2000 |
| 1 | Hyvarinen A.; Oja E.;, “Sparse code shrinkage for image denoising,” Neural Networks Proceedings - Hoyer - 1998 |
