Simple adaptive median filter for the removal of impulse noise from highly corrupted images

Ibrahim, Haidi; Kong, N.S.P.; Ng, Theam Foo

doi:10.1109/tce.2008.4711254

Cited by 170 publications

(105 citation statements)

References 16 publications

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“…We use both objective and subjective evaluations and begin with images containing various amounts of impulsive noise. Figure 6 shows the binarized images obtained after applying the SAM filter, the Sobel operator, thresholding, and correction [8], (e) EDB [6], (f) DWM [7], (g) SAM [5], and (h) proposed algorithm. [8], (e) EDB [6], (f) DWM [7], (g) SAM [5], and (h) proposed algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, if an image has large amounts of noise, the median filter is more likely to fail to restore noisy pixels on object boundaries compared to uniform regions, resulting in small random shifts of the object boundary. To solve this problem, the SAM filter [5] has been proposed to remove noise by adaptively adjusting mask size based on the quantity of noise. The SAM filter provides superior performance both for removing noise and also for preserving edges of an image with small amounts of noise.…”

Section: Existing Algorithmsmentioning

confidence: 99%

“…Filters that adaptively determine the appropriate mask size according to the quantity of noise, including the simple adaptive median (SAM) filter [5] and the efficient decisionbased (EDB) algorithm [6], have been proposed. Since these methods merely apply a filter according to noise quantity without considering the edge region, they produce serious distortions in edge regions corrupted by large amounts of noise.…”

Section: Introductionmentioning

confidence: 99%

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An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

Lee

2012

ETRI J

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This paper presents an algorithm for removing highdensity impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line-weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Existing Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

Lee

2012

ETRI J

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“…Simple adaptive median filter (SAMF) [13] is hybrid filter developed by combining the features of Adaptive median filter and switching median filter. Adaptive median filter provides flexibility of adapting variable size of filtering window according to local noise level in an image.…”

Section: Simple Adaptive Median Filtermentioning

confidence: 99%

Quality Enhancement of Video Denoising Algorithm with Combined Effect of Different Views

Ambulkar¹,

Golar²

2014

IOSRJCE

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“…There are many algorithms has been developed for the noise removal over past two decades. Among these algorithms, generally mean filters, median filters and their modified methods are used due to their good noise removal capacity and low computational cost benefits [2]- [7]. There are many algorithms like Pixel-Wise Median absolute Difference (PWMAD) [8], Rank Order Criteria (ROC) [9], and Switching-based Adaptive Weighted Mean (SAWM) filter [10] are uses a noise detection stage in order to achieve better results.…”

Section: Introductionmentioning

confidence: 99%

Modified Spatially Adaptive Denoising Algorithm for an Image Corrupted by Gaussian Noise

Dash¹,

Sathua²

2015

International Advanced Research Journal in Science, Engineering

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A modified spatially adaptive denoising algorithm for a single image corrupted by Gaussian noise is proposed in this paper. The proposed algorithm use local statistics of a selected window i.e. by defining local weighted mean, local weighted activity and local maximum. These local statistics are used to detect the noise in the image then a modified Gaussian filter is used for noise suppression. This algorithm is tested against different images and the experimental result shows its result is better than different existing methods like Pixel Wise Median Absolute Difference (PWMAD), Rank Order Criteria (ROC), Switching-based Adaptive Weighted Mean (SAWM) and Spatially Adaptive Denoising Algorithm (SADA).

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Simple adaptive median filter for the removal of impulse noise from highly corrupted images

Cited by 170 publications

References 16 publications

An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

An Edge-Based Adaptive Method for Removing High-Density Impulsive Noise from an Image While Preserving Edges

Quality Enhancement of Video Denoising Algorithm with Combined Effect of Different Views

Modified Spatially Adaptive Denoising Algorithm for an Image Corrupted by Gaussian Noise

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