Impulse Denoising Using Fuzzy and Directional Processing on a DSP
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images
IEEE Transactions on Image Processing
Geometric features-based filtering for suppression of impulse noise in color images
IEEE Transactions on Image Processing
Some improvements for image filtering using peer group techniques
Image and Vision Computing
Video Denoising by Fuzzy Directional Filter Using the DSP EVM DM642
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Peer group switching filter for impulse noise reduction incolor images
Pattern Recognition Letters
Two-step fuzzy logic-based method for impulse noise detection in colour images
Pattern Recognition Letters
A fuzzy filter for the removal of random impulse noise in image sequences
Image and Vision Computing
Fuzzy Directional (FD) Filter for impulsive noise reduction in colour video sequences
Journal of Visual Communication and Image Representation
Peer group and fuzzy metric to remove noise in images using heterogeneous computing
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing
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A new impulse noise reduction method for color images is presented. Color images that are corrupted with impulse noise are generally filtered by applying a grayscale algorithm on each color component separately or using a vector-based approach where each pixel is considered as a single vector. The first approach causes artefacts especially on edge and texture pixels. Vector-based methods were successfully introduced to overcome this problem. Nevertheless, they tend to cluster the noise and to receive a lower noise reduction performance. In this paper, we discuss an alternative technique which gives a good noise reduction performance while much less artefacts are introduced. The main difference between the proposed method and other classical noise reduction methods is that the color information is taken into account to develop (1) a better impulse noise detection method and (2) a noise reduction method that filters only the corrupted pixels while preserving the color and the edge sharpness. Experimental results show that the proposed method provides a significant improvement on other existing filters.