Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise
A novel method for the denoising of color videos corrupted by additive noise is presented in this paper. The proposed technique consists of three principal filtering steps: spatial, spatiotemporal, and spatial postprocessing. In contrast to other state-of-the-art algorithms, during the first spatial...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/758107 |
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| author | Volodymyr I. Ponomaryov Hector Montenegro-Monroy Luis Nino-de-Rivera Heydy Castillejos |
| author_facet | Volodymyr I. Ponomaryov Hector Montenegro-Monroy Luis Nino-de-Rivera Heydy Castillejos |
| author_sort | Volodymyr I. Ponomaryov |
| collection | DOAJ |
| description | A novel method for the denoising of color videos corrupted by additive noise is presented in this paper. The proposed technique consists of three principal filtering steps: spatial, spatiotemporal, and spatial postprocessing. In contrast to other state-of-the-art algorithms, during the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central pixel as well as the R, G, and B channel correlation between the analogous pixels in different color bands are taken into account. These gradient values give the information about the level of contamination then the designed fuzzy rules are used to preserve the image features (textures, edges, sharpness, chromatic properties, etc.). In the second step, two neighboring video frames are processed together. Possible local motions between neighboring frames are estimated using block matching procedure in eight directions to perform interframe filtering. In the final step, the edges and smoothed regions in a current frame are distinguished for final postprocessing filtering. Numerous simulation results confirm that this novel 3D fuzzy method performs better than other state-of-the-art techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM) as well as subjective perception via the human vision system in the different color videos. |
| format | Article |
| id | doaj-art-c8ff38cfaaaf47c68d4280c8d6464ad1 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-c8ff38cfaaaf47c68d4280c8d6464ad12025-02-03T07:25:56ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/758107758107Fuzzy Filtering Method for Color Videos Corrupted by Additive NoiseVolodymyr I. Ponomaryov0Hector Montenegro-Monroy1Luis Nino-de-Rivera2Heydy Castillejos3Instituto Politecnico Nacional, ESIME (Culhuacan), Avenida Santa Ana 1000, Colonia San Francisco Culhuacan, 04430 Ciudad de Mexico, DF, MexicoInstituto Politecnico Nacional, ESIME (Culhuacan), Avenida Santa Ana 1000, Colonia San Francisco Culhuacan, 04430 Ciudad de Mexico, DF, MexicoInstituto Politecnico Nacional, ESIME (Culhuacan), Avenida Santa Ana 1000, Colonia San Francisco Culhuacan, 04430 Ciudad de Mexico, DF, MexicoInstituto Politecnico Nacional, ESIME (Culhuacan), Avenida Santa Ana 1000, Colonia San Francisco Culhuacan, 04430 Ciudad de Mexico, DF, MexicoA novel method for the denoising of color videos corrupted by additive noise is presented in this paper. The proposed technique consists of three principal filtering steps: spatial, spatiotemporal, and spatial postprocessing. In contrast to other state-of-the-art algorithms, during the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central pixel as well as the R, G, and B channel correlation between the analogous pixels in different color bands are taken into account. These gradient values give the information about the level of contamination then the designed fuzzy rules are used to preserve the image features (textures, edges, sharpness, chromatic properties, etc.). In the second step, two neighboring video frames are processed together. Possible local motions between neighboring frames are estimated using block matching procedure in eight directions to perform interframe filtering. In the final step, the edges and smoothed regions in a current frame are distinguished for final postprocessing filtering. Numerous simulation results confirm that this novel 3D fuzzy method performs better than other state-of-the-art techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM) as well as subjective perception via the human vision system in the different color videos.http://dx.doi.org/10.1155/2014/758107 |
| spellingShingle | Volodymyr I. Ponomaryov Hector Montenegro-Monroy Luis Nino-de-Rivera Heydy Castillejos Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise The Scientific World Journal |
| title | Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise |
| title_full | Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise |
| title_fullStr | Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise |
| title_full_unstemmed | Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise |
| title_short | Fuzzy Filtering Method for Color Videos Corrupted by Additive Noise |
| title_sort | fuzzy filtering method for color videos corrupted by additive noise |
| url | http://dx.doi.org/10.1155/2014/758107 |
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