Name
Title | ||
|---|---|---|
Four-directional fractional-order total variation regularization for image denoising. |
Abstract | ||
|---|---|---|
Noise removal is a fundamental problem in image processing. Among many approaches, total variation (TV) has attracted great attention because of its advantage in preserving edges. However, it tends to exhibit some undesired staircase artifacts. Fractional-order TV (FTV) can overcome the drawback mentioned above, yet it does not take enough neighborhood information into account. An extension of FTV, four-directional FTV (FTV4) is put forward to explore more directional information of an image. We solve this FTV4 model by adopting the split Bregman algorithm and fast Fourier transform theory. An accelerated step is added in the algorithm to make it converge faster. To decrease the computation time, we introduce the convolution theory and calculate the matrix difference in the frequency domain instead of space domain. Experimental results show that the proposed image denoising model performs better than other state-of-the-art models in most cases. (C) 2017 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1117/1.JEI.26.5.053003 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
four-directional fractional-order total variation,fast Fourier transform,fast split Bregman,accelerated step,image denoising | Frequency domain,Pattern recognition,Convolution,Matrix (mathematics),Non-local means,Computer science,Image processing,Fast Fourier transform,Total variation denoising,Artificial intelligence,Computation | Journal |
Volume | Issue | ISSN |
26 | 5 | 1017-9909 |
Citations | PageRank | References |
1 | 0.35 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linna Wu | 1 | 1 | 0.35 |
| Yingpin Chen | 2 | 2 | 3.10 |
| Jiaquan Jin | 3 | 1 | 0.35 |
| Hongwei Du | 4 | 43 | 7.29 |
| Bensheng Qiu | 5 | 11 | 6.59 |