Adaptive Color Image Filtering Based on Center-Weighted Vector Directional Filters
Multidimensional Systems and Signal Processing
Fast detection and impulsive noise removal in color images
Real-Time Imaging - Special issue on multi-dimensional image processing
Impulse noise removal utilizing second-order difference analysis
Signal Processing
Cost-effective video filtering solution for real-time vision systems
EURASIP Journal on Applied Signal Processing
An impulsive noise color image filter using learning-based color morphological operations
Digital Signal Processing
Peer group switching filter for impulse noise reduction incolor images
Pattern Recognition Letters
Automatic micro-manipulation based on visual servoing
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
On the adaptive impulsive noise attenuation in color images
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
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A generalized version of the iterative conditional modes (ICM) method for image enhancement is developed. The proposed algorithm utilizes the characteristic of Markov random fields (MRF) in modeling the contextual information embedded in image formation. To cope with real images, a new local MRF model with a second-order neighborhood is introduced. This model extracts contextual information not only from the intensity levels but also from the relative position of neighboring cliques. Also, an outlier rejection method is presented. In this method, the rejection depends on each candidate's contribution to the local variance. To cope with a mixed noise case, a hypothesis test is implemented as part of the restoration procedure. The proposed algorithm performs signal adaptive, nonlinear, and recursive filtering. In comparing the performance of the new procedure with several well-known order statistic filters, the superiority of the proposed algorithm is demonstrated both in the mean-square-error (MSE) and the mean-absolute-error (MAE) senses. In addition, the new algorithm preserves the details of the images well. It should be noted that the blurring effect is not considered