An Improved Decision-Based Algorithm for Impulse Noise Removal

Nair, Madhu S.; Revathy, K.; Tatavarti, Rao

doi:10.1109/cisp.2008.21

Cited by 48 publications

(16 citation statements)

References 8 publications

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“…The results are compared with those of standard median filter (SMF) [1], DBA [20], MDBUTMF [21], [3], [11]. A quantitative comparison is done in terms of MSE, PSNR, IEF and computational time of the algorithms.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

Hanji¹,

Latte²

2015

IJCA

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“…The algorithms used in this paper are derived from the references cited in the square brackets below. The existing algorithms used for the comparison are standard median filter of window size 3 (SMF (3 × 3) [30], adaptive median filter (AMF) (W max = 39) [6], center-weighted median filter (CWF) [7], progressive switched median filter (PSMF) [10], modified decision based median filter (MDBMF) [16], alpha-trimmed mean filter (ATMF) (trimming factor is 4) [30], decision based algorithm (DBA) [13], cascaded filters (CUTMF, CUDBMPF) [15], IDBA [14] MDBMF [16] CUDMPF [15] MDBUTMF [17] MDBUMF-GM [18] ACBSA [20] AWMF [23] DBIDWIF modified decision based unsymmetric trimmed median filter (MDBUTMF) [17], improved decision based median filter (IDBA) [14], noise adaptive fuzzy switching median (NAFSM) [31], modified decision based unsymmetrical trimmed median filter with global mean MDBUTMF-GM [18], adaptive cardinal B-spline algorithm (ACBSA) [20], and adaptive weighted mean filter (AWMF) (W max = 39) [30]. All the algorithms used in the paper were tested on Kodak natural image database hosted in University of Southern California and Signal and Image Processing Institute website [32].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…This repeated replacement of preprocessed neighborhood results in streaking. The effect of streaking was minimized using an improved decision based algorithm [14] given by Madhu et al that used mean of preprocessed pixel which resulted in reduced streaks. A cascaded filters [15] were proposed by Balasubramanian et al for high density salt and pepper noise.…”

Section: Introductionmentioning

confidence: 99%

An adaptive decision based interpolation scheme for the removal of high density salt and pepper noise in images

Vasanth¹,

Ganesan²,

Harikrishna³

et al. 2017

J Image Video Proc.

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An adaptive decision based inverse distance weighted interpolation (DBIDWI) algorithm for the elimination of highdensity salt and pepper noise in images is proposed. The pixel is initially checked for salt and pepper noise. If classified as noisy pixel, replace it with an inverse distance weighted interpolation value. This interpolation estimates the values of corrupted pixels using the distance and values nearby non-noisy pixels in vicinity. Inverse distance weighted interpolation uses the contribution of non-noisy pixel to the interpolated value. The window size is varied adaptively depending upon the non-noisy content of the current processing window. The algorithm is tested on various images and found to exhibit good results both in terms of quantitative (PSNR, MSE, SSIM, Pratt's FOM) and qualitative (visually) at high noise densities. The algorithm performs very well in restoring an image corrupted by highdensity salt and pepper noise by preserving fine details of an image.

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“…Image Registration is the procedure of adjusting two or more images of same scene taken at various times, from various perspectives and/or by various sensors [14]. It is a procedure of changing diverse arrangement of information point into one co-ordinate framework.…”

Section: Methodology 31 Registrationmentioning

confidence: 99%

“…So the nature of the commotion lessening in images is measured by the factual amount measures: Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR). The execution of this channel on images spoiled with various commotions of different clamor levels is contrasted and Wiener separating system [14] Use neural network as the learning algorithm which follows the supervised learning. In this paper Bi-lateral filter is defined for its effectiveness in edge-preserved image Denoising.…”

Section: Introductionmentioning

confidence: 99%

Robust Algorithm for Super Resolution and Extracting Noise from DIP using Trimmed Median Filter

Kritika¹,

Rastogi²

2016

IJCA

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In the paper the most efficient model to implement super resolution using discrete wavelet transform is shown. The model is a three step process of image registration, interpolation and noise filtering using DWT. The paper resolutions the deduction of noise in the digital gray scale images. Normally, Data, text, picture, can be tainted by an additive noise with the process of the scanning. This method precludes the different type of noise such as Salt and Pepper noise (SP Noise) that reasons black and white spots in the original image. All the process is explained and simulation result is presented to prove the theory.

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An Improved Decision-Based Algorithm for Impulse Noise Removal

Cited by 48 publications

References 8 publications

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

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

An adaptive decision based interpolation scheme for the removal of high density salt and pepper noise in images

Robust Algorithm for Super Resolution and Extracting Noise from DIP using Trimmed Median Filter

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