Name
Abstract | ||
|---|---|---|
When using a regularized approach for image restoration there is always a compromise between image sharpness and noise suppression. Since noise is removed at the cost of edges and detail within the image, there is a need to introduce algorithms which exhibit some kind of memory and cater for the spatial structure of an image. To this cause, we introduce an efficient restoration algorithm, based on a modified adaptive Hopfield neural network. The algorithm is capable of spatially regularizing an image and thereby preserving data fidelity around edges while simultaneously suppressing noise in more noticeable areas such as smooth regions. The proposed method demonstrates an improvement in restoration quality over existing adaptive and non-adaptive approaches. This is illustrated with simulations on benchmark images under varying noise levels. |
Year | Venue | Keywords |
|---|---|---|
2002 | Journal of Intelligent and Fuzzy Systems | data fidelity,noise suppression,benchmark image,modified adaptive Hopfield neural,restoration quality,efficient restoration algorithm,suppressing noise,image sharpness,regularized image restoration,image restoration,varying noise level,spatially-adaptive neural network approach |
Field | DocType | Volume |
Computer vision,Noise suppression,Spatially adaptive,Artificial intelligence,Image restoration,Artificial neural network,Machine learning,Mathematics | Journal | 13 |
Issue | ISSN | Citations |
2-4 | 1064-1246 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex S. Palmer | 1 | 3 | 0.76 |
| Moe Razaz | 2 | 23 | 5.63 |
| Danilo Mandic | 3 | 1641 | 173.32 |