Directional Correlation-Dependent Filtering Technique for Removal of Impulse Noise

Venkata, G.; Reddy, Rami; Sujatha, B.

doi:10.14257/ijsip.2014.7.4.08

Cited by 2 publications

(1 citation statement)

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“…Work presented in [21] attempts at obtaining an integrated and consecutive original image from noisy image by a directional correlation-dependent filtering technique by removing salt and pepper noise from the corrupted images. The proposed algorithm can preserve edges very well while removing impulse noise and is very suitable to be applied to many real-time applications.…”

Section: Introductionmentioning

confidence: 99%

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In general, it is known that an adaptive filter adjusts its parameters iteratively such as size of the working window, decision threshold values used in two stage detection-estimation based switching filters, number of iterations etc. It is also known that nonlinear filters such as median filters and its several variants are popularly known for their ability in dealing with the unknown circumstances. In this paper an efficient and simple adaptive nonlinear filtering scheme is presented to eliminate the impulse noise from the digital images with an impulsive noise detection and reduction scheme based on adaptive nonlinear filter techniques. The proposed scheme employs image statistics based dynamically varying working window and an adaptive threshold for noise detection with a Noise Exclusive Median (NEM) based restoration. The intensity value of the Noise Exclusive Median (NEM) is derived from the processed pixels in local neighborhood of a dynamically adaptive window. In the proposed scheme use of an adaptive threshold value derived from the noisy image statistics returns more precise results for the noisy pixel detection. The proposed scheme is simple and can be implemented as either a single pass or a multi-pass with a maximum of three iterations with a simple stopping criterion. The goodness of the proposed scheme is evaluated with respect to the qualitative and quantitative measures obtained by MATLAB simulations with standard images added with impulsive noise of varying densities. From the comparative analysis it is evident that the proposed scheme out performs the state-of-art schemes, preferably in cases of high-density impulse noise.

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Section: Introductionmentioning

confidence: 99%