Name
Abstract | ||
|---|---|---|
Common vector approach (CVA) is an increasingly popular classification method in recognition problems where probability of having the dimensionality of the problem higher than the number of data items is not zero. In CVA, common component of the members of classes is separated from the discriminating difference parts and used to determine whether a given vector (a block of data) belongs to the class in question, or to find out the class it belongs to. In this study, overlapping image blocks near the current pixel to be denoised are used as input data and a class is constructed per pixel position. Denoised image block is then constructed with the sum of common vector of the class and difference vector of the centre block denoised by linear minimum mean square error estimation technique. Since the classes are formed using similar blocks, the edges are preserved while denoising the image. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-ipr.2014.0979 | Image Processing, IET |
Keywords | Field | DocType |
estimation theory,image classification,image denoising,mean square error methods,probability,vectors,CVA,common vector approach,image classification method,image denoising,image recognition,linear minimum mean square error estimation technique,probability | Noise reduction,Computer vision,Pattern recognition,Non-local means,Minimum mean square error,Curse of dimensionality,Pixel,Artificial intelligence,Image denoising,Mathematics | Journal |
Volume | Issue | ISSN |
9 | 8 | 1751-9659 |
Citations | PageRank | References |
2 | 0.37 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kemal Özkan | 1 | 11 | 4.28 |
| Erol Seke | 2 | 2 | 4.43 |