Name
Abstract | ||
|---|---|---|
Regularization method has been widely used in blind image restoration. Most regularization operators, however, are applied uniformly without considering difference of edge regions, which results in an unsolved trade-off conflict between smooth and edge regions. In this paper, we apply suitable regularization operators to smooth regions and edge regions respectively according to their characteristics instead of a global and constant one, and further employ the wavelet technique to control the noise amplification in order to improve the quality of divisional regularization. Experiment results show that our proposed Divisional Regularization and Wavelet Technique (DRWT) can deblur effectively without ringing effect in the restored images, thus improves edge restoration and reduces noise amplification. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.884 | ICNC |
Keywords | Field | DocType |
divisional regularization,experiment result,wavelet technique,regularization operator,regularization method,blind image,noise amplification,edge region,edge restoration,blind image restoration,suitable regularization operator,image restoration,estimation,wavelet transforms,wavelet,noise,psnr | Computer vision,Mathematical optimization,Pattern recognition,Computer science,Ringing,Regularization (mathematics),Artificial intelligence,Operator (computer programming),Image restoration,Wavelet,Wavelet transform | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lingwei Chen | 1 | 0 | 0.34 |
| Xinyu Chen | 2 | 29 | 7.43 |
| Ping Guo | 3 | 601 | 85.05 |