Fuzzy ordering theory and its use in filter generalization
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Steerable weighted median filters
IEEE Transactions on Image Processing
Modified switching median filter for impulse noise removal
Signal Processing
Switching bilateral filter with a texture/noise detector for universal noise removal
IEEE Transactions on Image Processing
A Separable Median Filter for Image Noise Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Accurate Computation of the Myriad Filter via Branch-and-Bound Search
IEEE Transactions on Signal Processing - Part II
Fast algorithms for weighted myriad computation by fixed-pointsearch
IEEE Transactions on Signal Processing
Affine order-statistic filters: “medianization” oflinear FIR filters
IEEE Transactions on Signal Processing
Image denoising: a nonlinear robust statistical approach
IEEE Transactions on Signal Processing
Optimality of the myriad filter in practical impulsive-noiseenvironments
IEEE Transactions on Signal Processing
Generalized Mean-Median Filtering for Robust Frequency-Selective Applications
IEEE Transactions on Signal Processing
Adaptive alpha-trimmed mean filters under deviations from assumed noise model
IEEE Transactions on Image Processing
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An iterative trimmed and truncated arithmetic mean (ITTM) algorithm is proposed, and the ITTM filters are developed. Here, trimming a sample means removing it and truncating a sample is to replace its value by a threshold. Simultaneously trimming and truncating enable the proposed filters to attenuate the mixed additive and exclusive noise in an effective way. The proposed trimming and truncating rules ensure that the output of the ITTM filter converges to the median. It offers an efficient method to estimate the median without time-consuming data sorting. Theoretical analysis shows that the ITTM filter of size n has a linear computational complexity O(n). Compared to the median filter and the iterative truncated arithmetic mean (ITM) filter, the proposed ITTM filter suppresses noise more effectively in some cases and has lower computational complexity. Experiments on synthetic data and real images verify the filter's properties.