Performance of bilateral filtering on Gaussian noise

Park, Jun‐Hee; Han, Jihee; Lee, Byung Uk

doi:10.1117/1.jei.23.4.043024

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Bilateral filter, regarded as an edge-preserving smoothing approach, tends to suppress noise while maintaining the prominent edges/patterns of the image through penalizing the smoothing function over the edges. [ 48 ] The bilateral filter, employed to reduce the noise levels in the different low-dose CT images, is mathematically formulated as…”

Section: Methodsmentioning

confidence: 99%

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Behrooz

Karimian

Mostafapour

et al. 2023

Journal of Medical Signals &Amp; Sensors

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Background: Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images. Methods: In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer–Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body. Results: The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and − 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = −6.15% ± 2.24%. Conclusions: The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.

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Section: Methodsmentioning

confidence: 99%

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Behrooz

Karimian

Mostafapour

et al. 2023

Journal of Medical Signals &Amp; Sensors

View full text Add to dashboard Cite

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“…From an empirical perspective, their study suggested that the optimal range parameter should be linearly proportional to the standard deviation of background noise and has a greater impact on the denoising performance than the spatial parameter in image denoising applications. Park et al [21] derived a close‐form equation that reveals the dependence of the noise reduction of the BF on the ratio of the range parameter to the noise standard deviation. This work confirms the empirical conclusion of Zhang and Gunturk [15].…”

Section: Related Workmentioning

confidence: 99%

Optimal weighted bilateral filter with dual‐range kernel for Gaussian noise removal

Luo

Yang

2020

IET Image Processing

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“…Elad 14 proved that the bilateral filter is a single Jacobi iteration of a weighted least-squares minimization. 15,16 To analyze the performance of the bilateral filter, Park et al 17 derived a closed-form equation of bilateral filtering for flat regions, which shows the relationship between noise reduction and filtering parameters. 15,16 To analyze the performance of the bilateral filter, Park et al 17 derived a closed-form equation of bilateral filtering for flat regions, which shows the relationship between noise reduction and filtering parameters.…”

Section: Spatial-domain Techniques: Bilateral Filteringmentioning

confidence: 99%

Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise

Kishan

Seelamantula

2015

J. Electron. Imaging

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We propose optimal bilateral filtering techniques for Gaussian noise suppression in images. To achieve maximum denoising performance via optimal filter parameter selection, we adopt Stein's unbiased risk estimate (SURE)-an unbiased estimate of the mean-squared error (MSE). Unlike MSE, SURE is independent of the ground truth and can be used in practical scenarios where the ground truth is unavailable. In our recent work, we derived SURE expressions in the context of the bilateral filter and proposed SURE-optimal bilateral filter (SOBF). We selected the optimal parameters of SOBF using the SURE criterion. To further improve the denoising performance of SOBF, we propose variants of SOBF, namely, SURE-optimal multiresolution bilateral filter (SMBF), which involves optimal bilateral filtering in a wavelet framework, and SURE-optimal patchbased bilateral filter (SPBF), where the bilateral filter parameters are optimized on small image patches. Using SURE guarantees automated parameter selection. The multiresolution and localized denoising in SMBF and SPBF, respectively, yield superior denoising performance when compared with the globally optimal SOBF. Experimental validations and comparisons show that the proposed denoisers perform on par with some state-of-the-art denoising techniques.

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Performance of bilateral filtering on Gaussian noise

Cited by 8 publications

References 13 publications

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Optimal weighted bilateral filter with dual‐range kernel for Gaussian noise removal

Patch-based and multiresolution optimum bilateral filters for denoising images corrupted by Gaussian noise

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