Adaptive noise Wiener filter for scanning electron microscope imaging system

Sim, K. S.; Teh, V.; Nia, M. E.

doi:10.1002/sca.21250

Cited by 22 publications

(10 citation statements)

References 8 publications

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“…A new Wiener based filter, named adaptive noise Wiener filter, was developed by Sim et al . (). In order to improve the performance of original Wiener filter, noise variance was included to it.…”

Section: Introductionmentioning

confidence: 97%

“…Many noise removal techniques have been introduced to enhance the quality of images corrupted by noise, but because of some characteristics of different techniques, for example, dependency on image structure, presence of different processing parameters, and degradation of image important details, they can only be used in some special fields. Several different noise reduction approaches have been proposed in SEM field (Herzog et al ., ; Yew and Pease, ; Lewis and Sakrison, ; Oron and Gilbert, ; Jones and Smith, ; Oho et al ., , ; Yano and Nomura, ; Sim et al ., , , ; Kiani et al ., ). Different filters use various approaches to achieve this aim.…”

Section: Introductionmentioning

confidence: 99%

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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.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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

Mazhari

Hasanzadeh

2016

Scanning

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“…With four commonly existing filters it matched the enactment of this novel filter namely adaptive noise Wiener (ANW) filter. Investigational conclusions demonstrate that the suggested new filter has enhanced performance on different noise variance comparing to the other existing noise removal filters in the experiments [27]. It proposed deblurring method which is performed by using the Wiener filter.…”

Section: E Wiener Filtermentioning

confidence: 99%

A Review on Noise and Denoise of Biometric Images

Shrivastava¹

2018

IJRASET

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In our work we mainly focused on denoising of biometric images such as MRI and USI images. In digital image processing, lessening in the noise is an essential procedure to interpret, analyze & enhance the main facts in an image. Still Lessening of Noise is a demanding task. Hence this study offers knowledge of noise and its types and also briefly defines the general denoising techniques for biometric images.

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“…For Transmission Electron Microscopy (TEM) images, several digital filters have been introduced by Kushwaha et al 7 such as median or Wiener filter. A similar work proposed by Sim et al 8 and Aguirre 9 based on employing an adaptive Wiener filter to enhance the effectiveness of the classical Wiener filter by considering the noise variance. Luisier et al 10 have suggested a Poisson Unbiased Risk Estimation-Linear Expansion of Thresholds (PURE-LET) technique for denoising images corrupted with Poisson noise.…”

Section: Introductionmentioning

confidence: 99%

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Nasser

Boudier

2018

Preprint

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Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Several nuclei segmentation approaches have been reported in the literature. However, most of them require fine-tuning of many parameters. Furthermore, they can only be applied to particular models or acquisition systems. As a result, there is a crucial need for a generic method to segment noisy and densely packed nuclei in microscopy images. In this paper, we present a sparse representation method for denoising of microscopy images. The proposed method works properly in the context of most challenging cases. In addition, it provides a denoised image and simultaneously the potential locations of nuclei. To evaluate the performance of the proposed method, we tested our method on two datasets from cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96.96% recall for C.elegans dataset. Besides, in Drosophila dataset, our method achieved very high recall (99.3%).

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Adaptive noise Wiener filter for scanning electron microscope imaging system

Cited by 22 publications

References 8 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

A Review on Noise and Denoise of Biometric Images

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

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