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. | |
| − | === | + | |
| + | === 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]] | + | [[mrcImageSmoothing]] supports it. |
| − | + | Merit: エッジが鈍らない.ごま塩型のNoise(ポアッソン型Noise)に強い. | |
| − | + | Demerit: 時間がかかる.演算が非線形である. | |
==== 可変加重平均法 ==== | ==== 可変加重平均法 ==== | ||
Revision as of 00:51, 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: エッジが鈍らない.ごま塩型のNoise(ポアッソン型Noise)に強い. Demerit: 時間がかかる.演算が非線形である.
可変加重平均法
局所領域における加重平均の重みを,それぞれの密度パターンによって適応的に変化させることにより,雑音を押さえることを目指したものである.
Edge and Line weights smoothing
Contrast-sensitive weights smoothing
Gradient inverse weighted smoothing
サブ局所領域分割法
局所領域を複数の局所領域に分割し,もっとも均一と思われるサブ領域を選択し,その領域をSmoothingする手法である.
エッジ保存Smoothing
矢村のSmoothingFilter
Lee-Sigma Filter(Lee-Sigma Filter)
局所領域の中で,指定した±Kσの範囲を超えているdata(特異値)を省いた平均値を代表点として用いる方法である. mrcImageSmoothingで対応している.
長所:エッジが鈍らない.ごま塩型のNoise(ポアッソン型Noise)に強い. 欠点:時間がかかる.演算が非線形である.
バイラテラルFilter(Bilateral Filter)
エッジを保存しながら,Smoothingすることを目的として,密度差がある領域での点拡がり関数を小さくする. mrcImageSmoothingで対応している.
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.
