Name
Title | ||
|---|---|---|
Fuzzy color video filtering technique for sequences corrupted by additive Gaussian noise |
Abstract | ||
|---|---|---|
In this paper, a novel framework is presented for the denoising of color video sequences corrupted by additive Gaussian noise. The proposed technique consists of three filtering stages: spatial, spatio-temporal, and spatial postprocessing. During the first spatial stage, the gradient values in eight directions for pixels located in the vicinity of a central pixel, as well as the interchannel correlation between the analogous pixels in different color bands (RGB), are taken into account. These gradient values that estimate the level of noise contamination are employed using the designed fuzzy rules to preserve the image features (e.g., textures, edges, sharpness, and chromatic properties). In the spatio-temporal denoising stage, two consecutive video frames are filtered together, thereby yielding more information. Additionally, small local motions between consecutive frames are estimated using block matching procedure in different directions, gathering interframe samples with similar features for efficient denoising. In the final stage, the edge and plain areas in a current frame are separated for different spatial postprocessing denoising. Two variants of proposed fuzzy filter, depending on sliding windows, are proposed. Additionally, a hybrid fuzzy-Wiener denoising technique is performed employing the proposed filtering approach. Numerous simulation results confirm that these novel fuzzy frameworks outperform other state-of-the-art techniques in terms of objective criteria, as well as subjective visual perception in the various color sequences. HighlightsThe framework consists of spatial, spatio-temporal and spatial postprocessing filtering stages.The proposed technique is based on fuzzy rules, gradient values, and interchannel correlations.The framework suppresses additive noise over a wide range of intensities preserving edges and fine features.The framework is extremely efficient in reproducing the chromatic characteristics of images.The scheme outperforms existing state-of-the-art algorithms in terms of objective and subjective criteria. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.12.025 | Neurocomputing |
Keywords | Field | DocType |
Color video denoising,Fuzzy logic rules,Additive noise,Color video sequences,Wiener denoising | Noise reduction,RGB color model,Artificial intelligence,Computer vision,Pattern recognition,Fuzzy logic,Filter (signal processing),Inter frame,Pixel,Gaussian noise,Video denoising,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
155 | C | 0925-2312 |
Citations | PageRank | References |
0 | 0.34 | 56 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Volodymyr I. Ponomaryov | 1 | 124 | 18.38 |
| Hector Montenegro-Monroy | 2 | 0 | 0.68 |
| Francisco J. Gallegos Funes | 3 | 63 | 10.29 |
| Oleksiy B. Pogrebnyak | 4 | 43 | 11.33 |
| Sergiy Sadovnychiy | 5 | 6 | 2.42 |