A new threshold-based median filtering technique for salt and pepper noise removal

Hanji, Geeta; Latte, Mrityunjaya V.

doi:10.1117/12.856333

Cited by 6 publications

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“…It has been observed that the window sizes needed for 10% to 90% noise density range between 3 to 9. Table 2 shows the better performance of the proposed NAF over the filter schemes described in [12], [17], [18] and [22] for low to high noise density cases.…”

Section: Resultsmentioning

confidence: 99%

Untitled

2014

IJBB

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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. KEYWORDSNonlinear filter, median based filter, Noise exclusive median (NEM), adaptive window, local neighborhood, impulse noise, Peak-Signal-to-Noise Ratio (PSNR)

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Section: Resultsmentioning

confidence: 99%

Untitled

2014

IJBB

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“…Set of figures 1,2 and 3 presented below are the restoration results obtained for 'Lena' and 'Baby' and 'Leaf' images corrupted with 70% noise density with the algorithms proposed [12], [17], [18] and [20] and the NMF. From the perceptual analysis, it is evident that the proposed algorithm performs with much better noise removal and detail preservation capability.…”

Section: Resultsmentioning

confidence: 99%

Novel Adaptive Filter (NAF) for Impulse Noise Suppression from Digital Images

Hanji¹,

Latte²

2014

IJBB

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“…A detailed survey of various methods reported in the literature to suppress several types of noise that gets added with the images during acquisition, transit, storage can be found from [6], [7], [8], [9], [10]. Since the work presented in this paper basically deals with the suppression of fixed valued impulsive noise along with the artifacts like drop lines, noise blotches etc., few of the important works reported in the literature are as below:…”

Section: Introduction and Literature Surveymentioning

confidence: 99%

A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images

Hanji¹,

Latte²

2015

IJCA

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A novel mean-median filter is proposed for the suppression of impulse noise and various artifacts from the digital images. Leading Diagonal Sorting Algorithm is used with the fixed 3x3 size working window to compute the median. Truncated mean is computed by defining the boundaries and truncating the pixel values in the filtering window that fall outside the defined boundary. Noise detection is carried in two steps: In the first step the 'reference pixel' is tested for the presence of impulses with Min-Max detection strategy. In the second stage of detection, edge preserving unique criteria is employed to further classify the pixel under test as 'noisy' or 'edge belonged'. This intelligent edge preserving decision criterion decides whether the test pixel deserves the restoration or not and facilitates the restoration of the noisy pixel either by the 'truncated mean' or by the 'window median' based on the decision threshold. Performance of the proposed filter algorithm is studied on a large number of images with varying amounts of salt and pepper noise and several types of artifacts and their combinations. Simulation results prove that the proposed algorithm (PA) effectively suppresses the high density salt and pepper noise (SPN) and in addition, it performs excellent in suppressing image artifacts such as strip lines (both white and black), drop lines (both white and black), missing bands and noise blotches. The novelty of the proposed mean-median filter lies in the fact that it attempts to simultaneously suppress impulsive noise and artifacts with good edge preservation (as the maximum size of the filtering window is restricted to '3×3' only) and also because a two stage detection mechanism is employed. The goodness of the proposed algorithm lies in replacing several independent filter schemes required for suppression of noise and artifacts of several types without much distorting the vital features of the image under test. The performance evaluation of the proposed algorithm is done in terms visual appearance and quantitative measures such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Image Enhancement Factor (IEF) and the Computation Time (CT). Simulation results are compared with other state of art algorithms to derive the meaningful conclusions. General TermsImage Processing, Noise and Artifacts Suppression.

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A new threshold-based median filtering technique for salt and pepper noise removal

Cited by 6 publications

References 0 publications

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Novel Adaptive Filter (NAF) for Impulse Noise Suppression from Digital Images

A Novel Mean Median Filter for Noise and Artifacts Suppression from Digital Images

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