4.10. NL Süzgeci

4.10.1. Genel Görünüm

Şekil 17.47. NL Süzgeci örneği

NL Süzgeci örneği

Özgün görüntü

NL Süzgeci örneği

NL Süzgeci uygulandı


NL, Doğrusal Olmayan anlamına gelir. Unix pnmnlfilt programından türetilmiştir, yumuşatma, beneksizleştirme ve keskinleştirme iyileştirme işlevlerini birleştirir. Seçim üzerinde değil, tüm katman üzerinde çalışır.

This is something of a swiss army knife filter. It has 3 distinct operating modes. In all of the modes each pixel in the image is examined and processed according to it and its surrounding pixels values. Rather than using 9 pixels in a 3×3 block, it uses a hexagonal block whose size can be set with the Radius option.

4.10.2. Süzgeci Etkinleştirme

Bu süzgeç ana menüde SüzgeçlerİyileştirNL Süzgeci… altında bulunur.

Etkin katmanın alfa kanalı varsa süzgeç çalışmaz. O zaman menü girdisi devre dışı bırakılır.

4.10.3. Seçenekler

Şekil 17.48. NL Süzgeci seçenekleri

“NL Süzgeci” seçenekleri

Ön izleme

İşaretlediğinde, parametre ayar sonuçları ön izlemede etkileşimli olarak görüntülenir.

Süzgeç

Çalışma Kipleri aşağıda açıklanmıştır.

Alfa

Controls the amount of the filter to apply. Valid range is 0.00-1.00. The exact meaning of this value depends on the selected operating mode. Note that this parameter is related to but not the same as the alpha parameter used in the pnmnlfilt program.

Yarıçap

Controls the size of the effective sampling region around each pixel. The range of this value is 0.33-1.00, where 0.33 means just the pixel itself (and thus the filter will have no effect), and 1.00 means all pixels in the 3×3 grid are sampled.

4.10.4. Çalışma Kipleri

Bu süzgeç birkaç farklı işlevi yerine getirebilir:

Alfa düzenlenmiş ortalama

The value of the center pixel will be replaced by the mean of the 7 hexagon values, but the 7 values are sorted by size and the top and bottom Alpha portion of the 7 are excluded from the mean. This implies that an Alpha value of 0.0 gives the same sort of output as a normal convolution (i.e. averaging or smoothing filter), where Radius will determine the strength of the filter. A good value to start from for subtle filtering is Alpha = 0.0, Radius = 0.55. For a more blatant effect, try Alpha = 0.0 and Radius = 1.0.

An Alpha value of 1.0 will cause the median value of the 7 hexagons to be used to replace the center pixel value. This sort of filter is good for eliminating pop or single pixel noise from an image without spreading the noise out or smudging features on the image. Judicious use of the Radius parameter will fine tune the filtering.

Intermediate values of Alpha give effects somewhere between smoothing and "pop" noise reduction. For subtle filtering try starting with values of Alpha = 0.8, Radius = 0.6. For a more blatant effect try Alpha = 1.0, Radius = 1.0 .

En iyi öngörü

This type of filter applies a smoothing filter adaptively over the image. For each pixel the variance of the surrounding hexagon values is calculated, and the amount of smoothing is made inversely proportional to it. The idea is that if the variance is small then it is due to noise in the image, while if the variance is large, it is because of wanted image features. As usual the Radius parameter controls the effective radius, but it probably advisable to leave the radius between 0.8 and 1.0 for the variance calculation to be meaningful. The Alpha parameter sets the noise threshold, over which less smoothing will be done. This means that small values of Alpha will give the most subtle filtering effect, while large values will tend to smooth all parts of the image. You could start with values like Alpha = 0.2, Radius = 1.0, and try increasing or decreasing the Alpha parameter to get the desired effect. This type of filter is best for filtering out dithering noise in both bitmap and color images.

Kenar iyileştirmesi

This is the opposite type of filter to the smoothing filter. It enhances edges. The Alpha parameter controls the amount of edge enhancement, from subtle (0.1) to blatant (0.9). The Radius parameter controls the effective radius as usual, but useful values are between 0.5 and 0.9. Try starting with values of Alpha = 0.3, Radius = 0.8.

Birlikte kullanım

The various operating modes can be used one after the other to get the desired result. For instance to turn a monochrome dithered image into grayscale image you could try one or two passes of the smoothing filter, followed by a pass of the optimal estimation filter, then some subtle edge enhancement. Note that using edge enhancement is only likely to be useful after one of the non-linear filters (alpha trimmed mean or optimal estimation filter), as edge enhancement is the direct opposite of smoothing.

For reducing color quantization noise in images (i.e. turning .gif files back into 24 bit files) you could try a pass of the optimal estimation filter (Alpha = 0.2, Radius = 1.0), a pass of the median filter (Alpha = 1.0, Radius = 0.55), and possibly a pass of the edge enhancement filter. Several passes of the optimal estimation filter with declining Alpha values are more effective than a single pass with a large Alpha value. As usual, there is a trade-off between filtering effectiveness and losing detail. Experimentation is encouraged.