Adaptive impulse detection using center-weighted median filters

Chen, T.; Wu, Hong Ren

doi:10.1109/97.889633

Cited by 634 publications

(344 citation statements)

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“…fixed valued impulse noise and random valued impulse noise. Its computational structure is simple [7].…”

Section: ) Adaptive Center Weighted Median Filter (Acwm)mentioning

confidence: 99%

“…Use of standard median filter for impulse noise suppression is described [8]. Adaptive center weighted median filter (ACWM) which is based on difference between output of center weighted median filter and the current pixel is presented [7]. A novel adaptive median filtering technique for the removal of random valued impulse noise is proposed in [2].…”

Section: Iiliterature Surveymentioning

confidence: 99%

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Suppression of Random Valued Impulse Noise in Image Processing: A Review

Pardeshi¹,

Patil²

2015

IJAREEIE

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Nowadays a good low complexity denoising technique is necessary as pre-processing operation in many real-time practical applications. Images get corrupted with impulse noise due to the process of image transmission and image acquisition. In the process of impulse noise filtering it is necessary to preserve edges and details of the image. Also to avoid image smoothing, only corrupted pixel must be filtered. Comprehensive survey of various denoising techniques has been focused in this paper. This paper illustrates the survey of different low complexity methods such as Median, Adaptive Center Weighted Median (ACWM), Adaptive Median Filter (AMF) and high complexity methods such as Alpha-trimmed Mean Based Method (ATMBM), Differential Rank Impulse Detector (DRID) and Rank Ordered Relative Difference (RORD).The most effective technique to remove random valued impulse noise without losing useful information with pleasing denoised image is by decision-tree based impulse detector and direction oriented edge preserving image filter. This design requires low computational cost, few memory buffers, no iterations and most suited to be applied to many real-time applications. Also this design can be efficiently designed with FPGA.KEYWORDS: corrupted pixel, denoising, direction oriented edge preserving image filter, impulse detector, random valued impulse noise. I.INTRODUCTIONDuring the process of image digitization, transmission and also due to malfunctioning pixel elements in the sensors of the camera, incorrect memory locations, and incorrect timing in analog-to-digital conversion, images are often corrupted by impulse noise. An important characteristic of this type of noise is that only parts of the pixels are corrupted and the rest are noise free. There are many applications in image processing such as face recognition, edge detection, medical imaging, scanning, printing, license plate detection where it is important to remove noise in the images before these subsequent processes. Noise in the image affects the subsequent process. Thus various techniques for removing impulse noise in images are described in this paper.Impulse noise is categorized into two methods based on distribution of the pixel values. The noise which has either minimum or maximum pixel value in grey scale image is called fixed valued impulse noise. It is also known as salt and pepper noise. The noise in which pixel values are uniformly distributed in the rang [0 255] in grey scale image is known as random valued impulse noise. Removal of salt and pepper noise in image is easy as compared with random valued impulse noise. There are most of the techniques which are reported till now works very well for salt and pepper noise but fails under random valued impulse noise. It is also observed that detection mechanism decides the performance of the filtering scheme. Thus better detector gives the good performance of filtering scheme. So performance of the detector is very important. The performance of the detector is depend on the threshold value whi...

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“…fixed valued impulse noise and random valued impulse noise. Its computational structure is simple [7].…”

Section: ) Adaptive Center Weighted Median Filter (Acwm)mentioning

confidence: 99%

Section: Iiliterature Surveymentioning

confidence: 99%

Suppression of Random Valued Impulse Noise in Image Processing: A Review

Pardeshi¹,

Patil²

2015

IJAREEIE

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show abstract

“…The median filter and its extensions are often effective to remove salt-and-pepper noise but can corrupt some textures. To reduce undesired corruption, various two-stage methods have been developed, first detecting the locations of noisy pixels, then recovering intensities only at noisy locations using certain filtering or variational methods, e.g., adaptive center weighted median filter (ACWMF) [4], rankordered absolute difference (ROAD) noise detector followed by a trilateral filtering [9], and a logarithmic version of the ROAD followed by edge-preserving regularization (EPR) for pixel restoration (ROLD-EPR) [7]. Both ROADtrilateral and ROLD-EPR methods can preserve edges better than median-type methods like ACWMF as they consider local structures during pixel restoration.…”

Section: Related Workmentioning

confidence: 99%

Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Wang

Trucco

2013

2013 IEEE International Conference on Computer Vision

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“…Despite their better efforts on smoothing the noise, they often tend to blur the fine details and alter the clean pixels. Therefore, switching based median filters are proposed to improve the performance of the median and order statistic based filters [11]- [17]. However, since the decision rule typically employs a single threshold for the whole image, the strategy of using such switching based filters may present difficulties in obtaining a robust decision measure, especially when the noise corruption is high.…”

Section: Introductionmentioning

confidence: 99%

Improved Radiometric Based Method for Suppressing Impulse Noise from Corrupted Images

Zhao

Chen

2010

IEICE Trans. Inf. & Syst.

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SUMMARYA novel filter is introduced in this paper to improve the ability of radiometric based method on suppressing impulse noise. Firstly, a new method is introduced to design the impulsive weight by measuring how impulsive a pixel is. Then, the impulsive weight is combined with the radiometric weight to obtain the evaluated values on each pixel in the whole corrupted image. The impulsive weight is mainly designed to suppress the impulse noise, while the radiometric weight is mainly designed to protect the noise-free pixel. Extensive experiments demonstrate that the proposed algorithm can perform much better than other filters in terms of the quantitative and qualitative aspects.

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Adaptive impulse detection using center-weighted median filters

Cited by 634 publications

References 8 publications

Suppression of Random Valued Impulse Noise in Image Processing: A Review

Suppression of Random Valued Impulse Noise in Image Processing: A Review

Single-Patch Low-Rank Prior for Non-pointwise Impulse Noise Removal

Improved Radiometric Based Method for Suppressing Impulse Noise from Corrupted Images

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