Name
Abstract | ||
|---|---|---|
Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2869461 | IEEE ACCESS |
Keywords | Field | DocType |
Denoising,Gaussian,non-local means,noise,Shapiro-Wilk test | Noise reduction,Shapiro–Wilk test,Noise measurement,Pattern recognition,Computer science,Non-local means,Image processing,Feature extraction,Image segmentation,Artificial intelligence,Gaussian noise,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wadageri Yamanappa | 1 | 0 | 0.34 |
| P. V. Sudeep | 2 | 27 | 3.44 |
| M. K. Sabu | 3 | 1 | 1.38 |
| Jeny Rajan | 4 | 113 | 18.07 |