Name
Abstract | ||
|---|---|---|
A no-reference image metric based on the singular value decomposition of local image gradients is proposed in this paper. This metric provides a quantitative measure of true image content, and reacts reasonably to both blur and random noise, so that it can be used in the automatic selection of parameters for image restoration algorithms, especially for denoising filters. Compared with GCV or SURE based approaches, this metric costs a small amount of computation, and does not require the noise to be Gaussian. Simulated and real data experiments demonstrated that our metric can capture the trend of quality change during the denoising process, and can yield parameters that show excellent visual performance in balancing between denoising and detail preservation. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5651376 | ICIP |
Keywords | Field | DocType |
image denoising,image restoration,sharpness,no-reference metric,gradient methods,denoising,parameter optimization,gaussian noise,local image gradients,no-reference image content metric,singular value decomposition,noise reduction,noise measurement,coherence,optimization | Noise reduction,Computer vision,Singular value decomposition,Pattern recognition,Noise measurement,Computer science,Non-local means,Gaussian,Artificial intelligence,Image restoration,Gaussian noise,Computation | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 4 |
PageRank | References | Authors |
0.43 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Zhu | 1 | 264 | 10.86 |
| Peyman Milanfar | 2 | 3284 | 155.61 |