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Removal of Additive Gaussian Noise by Complex Double Density Dual Tree Discrete Wavelet Transform
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lal, Shyam Chandra, Mahesh Upadhyay, Gopal Krishna Gupta, Deep |
| Copyright Year | 2011 |
| Abstract | This paper presents removal of additive gaussian noise by complex double density dual tree discrete wavelet Transform. However, wavelet coefficients of natural images have significant dependencies. For many natural signals, the wavelet transform is a more effective tool than the Fourier transform. The wavelet transform provides a multi resolution representation using a set of analyzing functions that are dilations and translations of a few functions (wavelets). In this paper we have evaluated & compared performances of Separable Dual Tree DWT (SDTDWT), Real Dual Tree DWT (RDTDWT), Complex Dual Tree DWT (CDTDWT), Standard Double Density DWT (SDDDTDWT), Real Double Density Dual Tree (RDDDTDWT) and Complex Double Density Dual Tree DWT (CDDDTDWT). Simulation and experimental results demonstrate that the complex double density dual tree discrete wavelet transform (CDDDTDWT) outperforms a number of other existing wavelet transform techniques and it is particularly effective for the very highly corrupted images. Keywords—DWT, SDTDWT, RDTDWT, CDTDWT, SDDDTDWT, RDDDTDWT & CDDDTDWT. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://mitpublications.org/yellow_images/1293679562_logo_journal3.pdf |
| Alternate Webpage(s) | http://www.mitpublications.org/yellow_images/1299467115_logo_journal3.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
