A new impulsive noise removal algorithm, the selective adaptive weighted median filter (SAWMF), is introduced. The proposed solution is a class of adaptive weighted median filters with incorporation of a switching mechanism. Using a median-based comparison technique to classify each image pixel as an impulse or a noise-free one, the new algorithm employs a weighted median filter where the weights are adaptively selected from two fixed values to restore the detected noisy pixels and keep the noise-free ones unchanged. The experimental results indicate that the SAWMF provides a significant performance improvement over many of the existing filtering techniques in suppressing impulsive noise with different contamination ratios.
This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional
masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and
classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering
operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The
simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image
details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures
and the perceptual visual quality.
Consensus building in group support systems relies on the mutual-question and mutual-elicitation of experts, so a
feedback mechanism is required to conduct experts to converge their thinking by visualizing the individual opinion and
the consistent state of the group. This paper proposes a new feedback mechanism, which first clusters the experts'
preferences into a set of subgroups, and then uses different line-types or line-colors to display the clustered opinions in
parallel coordinate. By using this mechanism, the group consistency is analyzed and the group discussion is conducted
efficiently. One of the characteristics of the proposed method is that it can protect the minority views automatically. An
example is presented to illustrate the application of the method.