Modified decision based median filter for impulse noise removal

Kunsoth, Raghuram; Biswas, Mantosh

doi:10.1109/wispnet.2016.7566350

Cited by 18 publications

(12 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utilization of local histograms for the calculations of m(y, x) and µ(y, x) may reduce the processing time needed, especially when image D is highly corrupted. As shown previously by the example in Figure 2, 5 , and h 7 by removing noise-free pixels (i.e., α 1 (j,k)=0) from the corresponding top row, and add noise-free pixels from the corresponding bottom row.…”

Section: Processing Block 3: Preliminary Noise Filteringmentioning

confidence: 99%

“…For k from N-5 to N-1: At the top row (i.e., j 1 =y-3), find noise-free pixel (α 1 (j 1 ,k)=0) and remove them from h 5 , i.e., h 5 (D(j 1 ,k))←h 5 (D(j 1 ,k))-1 At the bottom row (i.e., j 2 =y+2), find noise-free pixel (α 1 (j 2 ,k)=0) and add them to h 5 , i.e., h 5 (D(j 2 ,k))←h 5 (D(j 2 ,k))+1…”

Section: Endmentioning

confidence: 99%

“…salt), whereas the noise with minimum value appears as a black dot (i.e. pepper) [5,6]. This type of impulse noise can severely degrade the quality of the image, which * Correspondence: haidi_ibrahim@ieee.org may affect subsequent processes such as segmentation, detection, and classification, due to information lost [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Ibrahim¹,

Abdalameer²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degrades the quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique is required to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switching median filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. The implementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where the noise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by the second processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefined size, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally, the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter.Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the ability to restore images for up to 99% of the impulse noise corruption. As compared with the other five median filter-based methods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that the performance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption.However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. The outputs from IQASMF also have the best visual appearance.

show abstract

Section: Processing Block 3: Preliminary Noise Filteringmentioning

confidence: 99%

Section: Endmentioning

confidence: 99%

See 1 more Smart Citation

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Ibrahim¹,

Abdalameer²

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

show abstract

“…Most research working on removing noise from images depends on filters, such as standard median filter [22], [23], weighted median filter [23]- [25] and adaptive median filter [23], [26]. Some research worked based on modern methods such as non-LocalMean based methods [27], [28], PDE based methods [29], [30].…”

Section: Related Workmentioning

confidence: 99%

Image noise Detection and Removal based on Enhanced GridLOF Algorithm

Elmogy¹,

Mahmoud²,

Turki³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Image noise removal is a major task in image processing where noise can harness any information inferred from the image especially when the noise level is high. Although there exists many outlier detection approaches used for this task, more enhancements are needed to achieve better performance specifically in terms of time. This paper proposes a new algorithm to detect and remove noise from images depending on an enhanced version of GridLOF algorithm. The enhancement aims to reduce the time and complexity of the algorithm while attaining comparable accuracy. Simulation results on a set of different images proved that proposed algorithm achieves the standard accuracy.

show abstract

“…[14][15][16][17][18][19][20][21][22][23][24] proposed various modifications of the SMF which target reducing the running time of the SMF by either using the histogram based approach for the estimation of median values or approximation techniques to generate median values for the denoising task. A Fast Median Filter Approximation Proposed by [25] tackles the running time of the SMF to achieve denoising of impulse noise in real-time, while the Modified Decision Based Median Filter (MDBMF) proposed by [26] tackles the poor quality denoised images that are generated by the SMF. These denoising algorithms exhibit an interesting scenario of a trade-off between the quality of restored images and computational time or running time.…”

Section: Introductionmentioning

confidence: 99%

An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time

Appiah

Hayfron-Acquah

Asante

2020

AJRCoS

View full text Add to dashboard Cite

For computer vision systems to effectively perform diagnoses, identification, tracking, monitoring and surveillance, image data must be devoid of noise. Various types of noises such as Salt-and-pepper or Impulse, Gaussian, Shot, Quantization, Anisotropic, and Periodic noises corrupts images making it difficult to extract relevant information from them. This has led to a lot of proposed algorithms to help fix the problem. Among the proposed algorithms, the median filter has been successful in handling salt-and-pepper noise and preserving edges in images. However, its moderate to high running time and poor performance when images are corrupted with high densities of noise, has led to various proposed modifications of the median filter. The challenge observed with all these modifications is the trade-off between efficient running time and quality of denoised images. This paper proposes an algorithm that delivers quality denoised images in low running time. Two state-of-the-art algorithms are combined into one and a technique called Mid-Value-Decision-Median introduced into the proposed algorithm to deliver high quality denoised images in real-time. The proposed algorithm, High-Performance Modified Decision Based Median Filter (HPMDBMF) runs about 200 times faster than the state-of-the-art Modified Decision Based Median Filter (MDBMF) and still generate equivalent output.

show abstract

Modified decision based median filter for impulse noise removal

Cited by 18 publications

References 5 publications

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Image noise Detection and Removal based on Enhanced GridLOF Algorithm

An Effective Algorithm for Denoising Salt-And-Pepper Noise in Real-Time

Contact Info

Product

Resources

About