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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lerga, J. Sucic, V. Sersic, D. |
| Copyright Year | 2009 |
| Description | Author affiliation: University of Rijeka - Faculty of Engineering, Vukovarska 58, HR-51000, Croatia (Lerga, J.; Sucic, V.) || University of Zagreb - Faculty of Electrical Engineering and Computing, Unska 3, HR-10000, Croatia (Sersic, D.) |
| Abstract | The recently proposed signal denoising method based on the relative intersection of confidence intervals (RICI) rule combined with local polynomial approximation is applied to image denoising, with images being transformed to one-dimensional signals. The obtained results, in the terms of peak signal-to-noise ratio (PSNR), are compared to the results obtained using the approach based on the intersection of confidence (ICI) rule. It is shown that the denoising approach using the LPA-RICI method outperforms the one using the LPA-ICI method. Although the proposed denoising approach does not outperform the state-of-the-art denoising techniques, the main advantages of the one-dimensional image denoising algorithm are its simplicity, and the fact that it is computationally less demanding than two-dimensional anisotropic image denoising approaches. |
| Starting Page | 57 |
| Ending Page | 60 |
| File Size | 505942 |
| Page Count | 4 |
| File Format | |
| ISBN | 9789537044107 |
| ISSN | 13342630 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-28 |
| Publisher Place | Croatia |
| Access Restriction | Subscribed |
| Rights Holder | Croatian Society Electronic |
| Subject Keyword | Additive noise PSNR Digital images adaptive filtering Noise reduction Adaptive filters local polynomial approximation (LPA) Wavelet coefficients image restoration intersection of confidence intervals (ICI) rule Signal processing algorithms Filtering algorithms relative intersection of confidence intervals (RICI) rule adaptive varying window selection Polynomials Image denoising one-dimensional signal processing |
| Content Type | Text |
| Resource Type | Article |
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