Name
Abstract | ||
|---|---|---|
Most non-blind deconvolution methods are developed under the error-free kernel assumption, and are not robust to inaccurate blur kernel. Unfortunately, despite the great progress in blind deconvolution, estimation error remains inevitable during blur kernel estimation. Consequently, severe artifacts such as ringing effects and distortions are likely to be introduced in the non-blind deconvolution ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2017.2764261 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Kernel,Deconvolution,Estimation error,Robustness,Computational modeling | Kernel (linear algebra),Pattern recognition,Deblurring,Blind deconvolution,Deconvolution,Robustness (computer science),Fourier transform,Artificial intelligence,Mathematics,Kernel density estimation,Wavelet | Journal |
Volume | Issue | ISSN |
27 | 1 | 1057-7149 |
Citations | PageRank | References |
6 | 0.43 | 32 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongwei Ren | 1 | 103 | 12.26 |
| Wangmeng Zuo | 2 | 3833 | 173.11 |
| david zhang | 3 | 445 | 30.69 |
| Jun Xu | 4 | 156 | 9.95 |
| Lei Zhang | 5 | 16326 | 543.99 |