Name
Abstract | ||
|---|---|---|
Based on an integration of a simple impulse detector and a robust neuro-fuzzy (RNF) network, a new effective filter for color images corrupted by impulse noise is presented in this paper. There are two distinctive properties of the proposed method. The first is the use of a simple impulse detector. The other is the use of a novel membership function in the design of the adaptive RNF network, making the network robust to impulse noise. Extensive simulation results indicate that the proposed filter not only has the ability of noise attenuation but also has desirable robustness. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/FSKD.2007.74 | FSKD (4) |
Keywords | Field | DocType |
desirable robustness,new filter,robust neuro-fuzzy,simple impulse detector,color image,proposed filter,new effective filter,adaptive rnf network,impulse noise,noise attenuation,membership function,learning artificial intelligence,neuro fuzzy | Computer vision,Raised-cosine filter,Digital filter,Linear filter,Computer science,Salt-and-pepper noise,Impulse (physics),Robustness (computer science),Impulse noise,Artificial intelligence,Detector,Machine learning | Conference |
ISBN | Citations | PageRank |
0-7695-2874-0 | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yueyang Li | 1 | 124 | 12.98 |
| Bin Han | 2 | 11 | 2.23 |
| Shitong Wang | 3 | 1485 | 109.13 |
| Fu-lai Chung | 4 | 244 | 34.50 |