Name
Abstract | ||
|---|---|---|
Dyadic Curvelet transform (DClet) is proposed as a tool for image processing and computer vision. It is an extended curvelet transform that solves the problem of conventional curvelet, of decomposition into components at different scales. It provides simplicity, dyadic scales, and absence of redundancy for analysis and synthesis objects with discontinuities along curves, i.e., edges via directional basis functions. The performance of the proposed method is evaluated by removing Gaussian, Speckles, and Random noises from different noisy standard images. Average 26.71 dB Peak Signal to Noise Ratio (PSNR) compared to 25.87 dB via the wavelet transform is evidence that the DClet outperforms the wavelet transform for removing noise. The proposed method is robust, which makes it suitable for biomedical applications. It is a candidate for gray and color image enhancement and applicable for compression or efficient coding in which critical sampling might be relevant. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.20965/jaciii.2007.p0641 | JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS |
Keywords | Field | DocType |
image processing, curvelet, noise reduction, wavelet, ridgelet | Noise reduction,Image noise reduction,Curvelet transform,Pattern recognition,Computer science,Image processing,Artificial intelligence,Curvelet,Wavelet | Journal |
Volume | Issue | ISSN |
11 | 6 | 1343-0130 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marjan Sedighi Anaraki | 1 | 0 | 0.68 |
| Fangyan Dong | 2 | 453 | 54.77 |
| Hajime Nobuhara | 3 | 192 | 34.02 |
| Kaoru Hirota | 4 | 1634 | 195.49 |