Name
Abstract | ||
|---|---|---|
Image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, state-of-the-art denoising algorithms have been clearly dominated by nonlocal patch-based methods, which explicitly exploit patch self-similarity within the targeted image. However, in the past two years, discriminatively trained local approaches have started to outperform previous nonlocal models and have been attracting increasing attention due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on the filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD models become less effective when the noise level is high and consequently intro... |
Year | Venue | DocType |
|---|---|---|
2017 | SIAM J. Imaging Sciences | Journal |
Volume | Issue | Citations |
abs/1609.06585 | 3 | 0 |
PageRank | References | Authors |
0.34 | 3 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| WenSen Feng | 1 | 36 | 5.30 |
| Qiao Peng | 2 | 20 | 12.17 |
| Xi Xuanyang | 3 | 62 | 4.66 |
| Yunjin Chen | 4 | 407 | 14.89 |