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Adaptive k-means clustering for improving non- local means filtering.
| Content Provider | CiteSeerX |
|---|---|
| Author | Nandini Prasad K., S. Prabha, R. Pushpalatha, S. |
| Abstract | Recording devices whether analog or digital, have traits which make them susceptible to noise. In selecting a noise reduction algorithm, one must weigh several factors. Image denoising is defined as a method to recover a true image from an observed noisy image and is applied in display systems to improve the quality of image. One of the popular denoising methods, NLM, produces the quality of image compared than other denoising methods. We propose to improve non local means and using rotationally invariant block matching (RIBM) into the NLM framework. NLM applies moment invariants based K-means clustering on the Gaussian blurred image, which provides better classification before weighted averaging. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Non Local Mean Adaptive K-means Local Mean Several Factor Observed Noisy Image True Image Popular Denoising Method Weighted Averaging Image Denoising Gaussian Blurred Image Invariant Block Matching Noise Reduction Algorithm Moment Invariant Nlm Framework Display System |
| Content Type | Text |
