Detectors of the impulsive noise and new effective filters for the impulsive noise reduction

Abstract

As it is known, the impulsive noise appears on the image in the form of randomly distributed pixels of random brightness. Impulses themselves usually differ much from the surrounding pixels in brightness. The main topic of the paper is the introduction of the new impulse detection criteria, and their application to such filters as median, rank-order and cellular neural Boolean. Three impulse detectors are considered. The Rank Impulse Detector uses such property of impulse that its rank in variation series is usually quite different from rank of the median. Exponential Median Detector uses the exponent of the difference between the local median and the value of pixel to detect the impulse. Combination of these two detectors forms the Enhanced Rank Impulse Detector and integrates advantages of both of them. In combination with filter it allows iterative filtering without further image destruction.

Multichannel image processing by use of median M-type L-filter

“…For this reason we implement two versions of the proposed impulsive noise detector. The first impulsive noise detector is used when the impulsive noise percentage is low, it is defined as [13],…”

“…This filter utilizes vector approach and the Median M-type (MM) estimator with simple cut, Andrew sine, and Tukey biweight influence functions [10][11][12] in the filtering scheme of L-filter [4] to obtain sufficient noise suppression for each channel of an color image. We also introduce the use of impulsive noise detectors [13] to improve the properties of noise suppression and detail preservation in the proposed filtering scheme in the case of low and high densities of impulsive noise. To demonstrate the performance of the proposed filtering scheme in real applications, we applied it for filtering of Ku and UHF band SAR images, which naturally have speckle noise.…”

Segmentation of Noisy Images Using the Rank M-type L-filter and the Fuzzy C-Means Clustering Algorithm

“…To improve the properties of impulsive noise suppression of the proposed filter we introduce an impulse detector, this one chooses that pixel is or not filter. The impulsive detector used is defined as [12]: (10) where, is the central pixel in the filtering window, > 0 y 0 are thresholds, is the length of the data and is the median of pixels into the filtering window.…”

Vector Median M-Type L Filter to Process Multichannel Images

“…The redescending M-estimator with different influence functions is combined with the R-(median) estimator into the L filtering scheme to obtain sufficient impulsive noise suppression for each channel by using the vector approach. We also introduce the use of an impulsive noise detector [9] to improve the properties of noise suppression and detail preservation in the proposed filtering scheme.…”