Difference between revisions of "Smoothing"
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=== Smoothing without lack of Image quality(edge, line) === | === Smoothing without lack of Image quality(edge, line) === | ||
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==== Median Filter ==== | ==== Median Filter ==== | ||
This is a filter which treats a medium value in specified window as the representative value. It is non linear filter. | This is a filter which treats a medium value in specified window as the representative value. It is non linear filter. | ||
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Demerit: It needs to spend many time. The calculation is nonlinear. | 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 () 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. |
==== 非対称拡散Filter(Anisotropic Diffusion)==== | ==== 非対称拡散Filter(Anisotropic Diffusion)==== | ||
Revision as of 02:12, 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 弛緩法
- 3 Frequency領域での処理によるSmoothing
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 () 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.
非対称拡散Filter(Anisotropic Diffusion)
弛緩法
画素の濃淡レベルを確率として取り扱い,繰り返し計算を通して,最終的に適切な解をえる.
Frequency領域での処理によるSmoothing
ローパスFilter
ガウス型NoiseなどのNoiseが,Spatial frequencyの低周波成分から高周波成分まで比較的同定の強度をもつのに対して,信号である画像は,一般に,低周波成分の強度が高く,高周波成分の強度が低い傾向がある.そのため,高周波成分を抑えることにより,SN比の向上を図ることを目的としたFilterである.
長所:比較的高速である.線形演算である. 短所:エッジが鈍る.
