A filter for homogeneous areas in very high resolution SAR images based on hysteresis smoothing

Anahara, Takuma; Schmitt, Michael; Tamura, Masayuki

doi:10.1109/igarss.2012.6350641

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…Considering a noisy image ( y ) as,

y = x + n

where x is original image and n is zero mean additive white Gaussian noise, the goal of SHS is to obtain an estimate of x from y . Following rule can be used to describe SHS (Anahara et al ., ):

{true \overset{x}{true}}_{i + 1} = true{\begin{matrix} center \\ center y_{i + 1} - \frac{C W}{2} & center if {\hat{x}}_{i} + \frac{C W}{2} < y_{i + 1} \\ center {true \overset{x}{true}}_{i} & center if {\hat{x}}_{i} - \frac{C W}{2} \leq y_{i + 1} \leq {\hat{x}}_{i} + \frac{C W}{2} \\ center y_{i + 1} + \frac{C W}{2} & center if y_{i + 1} < {\hat{x}}_{i} - \frac{C W}{2} \end{matrix}

where y i is intensity value of i ‐th pixel in the noisy image and

{true \overset{x}{true}}_{i}

is the smoothed value of i ‐th pixel. CW denotes the cursor width and is equal to the size of the largest intensity value variation which should be removed.…”

Section: Current Hysteresis Smoothing Methodsmentioning

confidence: 99%

Suppression of noise in SEM images using weighted local hysteresis smoothing filter

Mazhari

Hasanzadeh

2016

Scanning

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It has been proven that Hysteresis Smoothing (HS) has several advantages for Scanning Electron Microscopy (SEM) image noise reduction. HS uses hysteresis thresholding to remove noise besides preserving important details of images. Determination of optimal threshold values (cursor width) plays an effective role in improving the performance of HS based filters. Recently, a novel local technique, named Local Adaptive Hysteresis Smoothing (LAHS), has been proposed to compute an optimal cursor width. In this paper, a new method is proposed to improve the performance of LAHS in noise reduction and detail preservation. In the proposed approach which is based on weighted averaging, local statistical characteristics of the image are used in order to modify the final values of estimated pixels by LAHS method. Proposed method is applied to SEM images corrupted by different levels of noise. Noise reduction and detail preservation performance of the proposed method is compared in both objective and subjective manners with other HS based filters. Experimental results demonstrate that the proposed method is successful in improving the performance of LAHS and also it achieves better performance in noise reduction besides detail preservation of SEM images in comparison with other HS based filters. SCANNING 38:634-643, 2016. © 2016 Wiley Periodicals, Inc.

show abstract

“…Considering a noisy image ( y ) as,

y = x + n

where x is original image and n is zero mean additive white Gaussian noise, the goal of SHS is to obtain an estimate of x from y . Following rule can be used to describe SHS (Anahara et al ., ):

{true \overset{x}{true}}_{i + 1} = true{\begin{matrix} center \\ center y_{i + 1} - \frac{C W}{2} & center if {\hat{x}}_{i} + \frac{C W}{2} < y_{i + 1} \\ center {true \overset{x}{true}}_{i} & center if {\hat{x}}_{i} - \frac{C W}{2} \leq y_{i + 1} \leq {\hat{x}}_{i} + \frac{C W}{2} \\ center y_{i + 1} + \frac{C W}{2} & center if y_{i + 1} < {\hat{x}}_{i} - \frac{C W}{2} \end{matrix}

where y i is intensity value of i ‐th pixel in the noisy image and

{true \overset{x}{true}}_{i}

is the smoothed value of i ‐th pixel. CW denotes the cursor width and is equal to the size of the largest intensity value variation which should be removed.…”

Section: Current Hysteresis Smoothing Methodsmentioning

confidence: 99%

Suppression of noise in SEM images using weighted local hysteresis smoothing filter

Mazhari

Hasanzadeh

2016

Scanning

View full text Add to dashboard Cite

show abstract

A filter for homogeneous areas in very high resolution SAR images based on hysteresis smoothing

Cited by 1 publication

References 14 publications

Suppression of noise in SEM images using weighted local hysteresis smoothing filter

Suppression of noise in SEM images using weighted local hysteresis smoothing filter

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