Difference between revisions of "Smoothing"
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==== Local Average Filter ==== | ==== Local Average Filter ==== | ||
Average pixels in specified window. | Average pixels in specified window. | ||
| − | [[Eos]] | + | [[Eos]] can perform by using [[mrcImageSmoothing]] (-m 2), or [[mrcImageConvolution]] with suitable kernel. |
==== Local Weighted Average Filter ==== | ==== Local Weighted Average Filter ==== | ||
| − | + | Local Average Filter deals uniformly pixels in specified window. Against it, this filter greatly affects pixels near the centre. | |
| − | [[mrcImageConvolution]] | + | You can perform by using [[mrcImageConvolution]] with suitable kernel. |
| − | + | In Addition, it supports convolution of Gaussian Function. In this case, generally use a Windowing Function as ±2σ ~±3σ. | |
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| − | + | === Smoothing without lack of Image quality(edge, line) === | |
| − | + | ==== Median Filter ==== | |
| + | This is a filter which treats a medium value in specified window as the representative value. It is non linear filter. | ||
| + | [[mrcImageSmoothing]] supports it. | ||
| − | ==== | + | Merit: Edge doesn't become almost dull. Good at removing the salt-and-pepper noise (Poisson noise). |
| − | + | Demerit: It needs to spend many time. The calculation is nonlinear. | |
| + | |||
| + | ==== Variable weighted averaging method ==== | ||
| + | This purpose is relaxation of noise by adaptively changing weight of weighted average in local region depending on each density pattern. | ||
===== Edge and Line weights smoothing ===== | ===== Edge and Line weights smoothing ===== | ||
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===== Gradient inverse weighted smoothing ===== | ===== Gradient inverse weighted smoothing ===== | ||
| − | ==== | + | ==== Sub local region splitting method ==== |
| − | + | This is a method that splits a local region to multiple local region, and selects a most uniform subregion, and performs smoothing at the region. | |
| − | ===== | + | ===== Edge preserving smoothing ===== |
| − | ===== | + | ===== Smoothing Filter by Yamura ===== |
| − | ==== Lee-Sigma Filter | + | ==== Lee-Sigma Filter ==== |
| − | + | This is a method which treats the mean about local region without data over specified range ±Kσ (Unique point) as a representative value. | |
| − | [[mrcImageSmoothing]] | + | [[mrcImageSmoothing]] supports it. |
| − | + | Merit: Edge doesn't become almost dull. Good at removing the salt-and-pepper noise (Poisson noise). | |
| − | + | Demerit: It needs to spend many time. The calculation is nonlinear. | |
| − | ==== | + | ==== Bilateral Filter==== |
| − | + | This is used for smoothing preserving edge, and the Point Spread Function([[PSF]]) on region that has great difference among its densities is reduced. | |
| − | [[mrcImageSmoothing]] | + | [[mrcImageSmoothing]] supports it. |
| − | ==== | + | ==== Anisotropic Diffusion Filter ==== |
| − | == | + | == Relaxation == |
| − | + | This method treats grayscale level as probability, and get a suitable solution by repeating calculation finally. | |
| − | == | + | == Smoothing by process at frequency regions == |
| − | === | + | === Low-Pass Filter === |
| − | + | Noise(e.g. Gaussian Noise) has intensity to same extent for each frequency component in a spatial frequency. Against it, generally a signal has large intensity at low frequency component, and has small intensity at high frequency component. For it, there is this filter that improves the SN ratio by reducing the high frequency component. | |
| − | + | Merit: comparatively high speed. Linear calculation.<br> | |
| − | + | Demerit: Edge become sometimes dull. | |
Latest revision as of 02:45, 8 October 2014
Smoothing is a method to restore image from degradation by noise. Perform preprocess to remove the noise, then the image can be analyzed properly.
Contents
- 1 The method by using local operator
- 2 Relaxation
- 3 Smoothing by process at frequency regions
The method by using local operator
The method by using local operator convolution is a most basic calculation to remove noise.
Simple Average
Local Average Filter
Average pixels in specified window. Eos can perform by using mrcImageSmoothing (-m 2), or mrcImageConvolution with suitable kernel.
Local Weighted Average Filter
Local Average Filter deals uniformly pixels in specified window. Against it, this filter greatly affects pixels near the centre. You can perform by using mrcImageConvolution with suitable kernel. In Addition, it supports convolution of Gaussian Function. In this case, generally use a Windowing Function as ±2σ ~±3σ.
Smoothing without lack of Image quality(edge, line)
Median Filter
This is a filter which treats a medium value in specified window as the representative value. It is non linear filter. mrcImageSmoothing supports it.
Merit: Edge doesn't become almost dull. Good at removing the salt-and-pepper noise (Poisson noise). Demerit: It needs to spend many time. The calculation is nonlinear.
Variable weighted averaging method
This purpose is relaxation of noise by adaptively changing weight of weighted average in local region depending on each density pattern.
Edge and Line weights smoothing
Contrast-sensitive weights smoothing
Gradient inverse weighted smoothing
Sub local region splitting method
This is a method that splits a local region to multiple local region, and selects a most uniform subregion, and performs smoothing at the region.
Edge preserving smoothing
Smoothing Filter by Yamura
Lee-Sigma Filter
This is a method which treats the mean about local region without data over specified range ±Kσ (Unique point) as a representative value. mrcImageSmoothing supports it.
Merit: Edge doesn't become almost dull. Good at removing the salt-and-pepper noise (Poisson noise). Demerit: It needs to spend many time. The calculation is nonlinear.
Bilateral Filter
This is used for smoothing preserving edge, and the Point Spread Function(PSF) on region that has great difference among its densities is reduced. mrcImageSmoothing supports it.
Anisotropic Diffusion Filter
Relaxation
This method treats grayscale level as probability, and get a suitable solution by repeating calculation finally.
Smoothing by process at frequency regions
Low-Pass Filter
Noise(e.g. Gaussian Noise) has intensity to same extent for each frequency component in a spatial frequency. Against it, generally a signal has large intensity at low frequency component, and has small intensity at high frequency component. For it, there is this filter that improves the SN ratio by reducing the high frequency component.
Merit: comparatively high speed. Linear calculation.
Demerit: Edge become sometimes dull.
