Abstract
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.
Highlights
Image filtering has a wide field of applications such as image processing, computer vision, telecommunications, medicine, satellite imaging, and robots, where the main objective of the denoising procedure is to detect, filter, or remove undesired noise from a color image and videos
In the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central one as well as the R, G, and B channel correlation between analogous pixels are used, where the degree of contamination is estimated by employing novel fuzzy rules that allow for better preservation of image features
Numerous simulation results obtained for color videos with different texture characteristics, edges, color properties, and local motions have confirmed the superiority of this novel 3D fuzzy framework over other filtering techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM) as well as the subjective perception via human visual system
Summary
Image filtering has a wide field of applications such as image processing, computer vision, telecommunications, medicine, satellite imaging, and robots, where the main objective of the denoising procedure is to detect, filter, or remove undesired noise from a color image and videos. In the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central one as well as the R, G, and B channel correlation between analogous pixels are used, where the degree of contamination is estimated by employing novel fuzzy rules that allow for better preservation of image features. Numerous simulation results obtained for color videos with different texture characteristics, edges, color properties, and local motions have confirmed the superiority of this novel 3D fuzzy framework over other filtering techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM) as well as the subjective perception via human visual system. This paper is structured as follows: in Section 2 the proposed filter is explained; Section 3 presents and analyzes the simulation results and shows the performance evaluation and the experimental results are discussed; in Section 4 conclusions are presented
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