Francisco J. Gallegos-Funes

TL;DR: The capability and real-time processing features of a robust filter for the removal of impulsive noise in image processing applications and extensive simulation results have demonstrated that the proposed filter consistently outperforms other filters by balancing the tradeoff between noise suppression and fine detail preservation.

TL;DR: A new class of multichannel filters, vector rank M-type K-nearest neighbor (VRMKNNF) filters that can obtain the balance between noise suppression, and edge and fine detail preservation, especially in color image restoration are proposed.

TL;DR: Two enhanced Fuzzy C-Means (FCM) clustering algorithms with spatial constraints for noisy color image segmentation with robustness and effectiveness in the presence and absence of noise are introduced.

TL;DR: The proposed color index based thresholding method for background and foreground segmentation of plant images outperforms other algorithms used as comparative in plant images obtaining a segmentation error of 6.62 ± 5.85% and a classification ratio of 1.05.

TL;DR: The median M-type K-nearest neighbour (MM-KNN) filter to remove impulse noise from corrupted images is presented and simulation results have shown that the restoration performance is better than that of other known filters.