A New Method for Sharpening Color Images Using Fuzzy Approach
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Efficient impulse noise reduction via local directional gradients and fuzzy logic
Fuzzy Sets and Systems
Noisy image restoration based on boundary resetting BDND and median filtering with smallest window
WSEAS Transactions on Signal Processing
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
A new fuzzy impulse noise detection method for colour images
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Fuzzy directional adaptive recursive temporal filter for denoising of video sequences
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Fuzzy vector directional filters for multichannel image denoising
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A novel fuzzy color median filter based on an adaptive cascade of fuzzy inference systems
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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
Direction based adaptive weighted switching median filter for removing high density impulse noise
Computers and Electrical Engineering
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A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image