From 1017cbebe5d5edd859e0fddad0a8600f509f4821 Mon Sep 17 00:00:00 2001 From: giraffedata Date: Mon, 25 Dec 2006 03:06:05 +0000 Subject: Place user guide into Subversion repository git-svn-id: http://svn.code.sf.net/p/netpbm/code/userguide@181 9d0c8265-081b-0410-96cb-a4ca84ce46f8 --- pnmnlfilt.html | 178 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 pnmnlfilt.html (limited to 'pnmnlfilt.html') diff --git a/pnmnlfilt.html b/pnmnlfilt.html new file mode 100644 index 00000000..401472c0 --- /dev/null +++ b/pnmnlfilt.html @@ -0,0 +1,178 @@ + +Pnmnlfilt User Manual + +

pnmnlfilt

+Updated: 24 October 2006 +
+Table Of Contents + +

NAME

+ +pnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal +estimation smoothing, edge enhancement. + +

SYNOPSIS

+ +pnmnlfilt +alpha +radius +[pnmfile] + +

DESCRIPTION

+ +

This program is part of Netpbm. + +

pnmnlfilt produces an output image where the pixels are a +summary of multiple pixels near the corresponding location in an input +image. + +

This program works on multi-image streams. + +

This is something of a swiss army knife filter. It has 3 distinct +operating modes. In all of the modes pnmnlfilt examines each +pixel in the image and processes it according to the values of it and +its surrounding pixels. Rather than using a square block of +surrounding pixels (e.g. the subject pixel and its 8 immediate +neighbors, in a 3x3 square), pnmnlfilt uses 7 hexagonal areas. +You choose the size of the hexagons with the radius parameter. A +radius value of 1/3 means that the 7 hexagons essentially fit into the +subject pixel (ie. there will be no filtering effect). A radius +value of 1.0 means that the 7 hexagons essentially cover the 3x3 +immediate neighbor square. + +

Your choice of "alpha" parameter selects among the three +modes. + +

+Alpha trimmed mean filter (0.0 <= alpha <= 0.5)

+ +

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 (ie. 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 0.5 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 = 0.4, radius = 0.6 For a more +blatant effect try alpha = 0.5, radius = 1.0 + +

+Optimal estimation smoothing. (1.0 <= alpha <= 2.0)

+ +

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 = 1.2, 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. + +

Edge enhancement. (-0.1 >= alpha >= -0.9)

+ +

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 + +

Combination use.

+ +

The various modes of pnmnlfilt can be used one after the +other to get the desired result. For instance to turn a monochrome +dithered image into a 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 (ie. turning .gif +files back into 24 bit files) you could try a pass of the optimal +estimation filter (alpha 1.2, radius 1.0), a pass of the median filter +(alpha 0.5, 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 tradeoff between filtering +effectiveness and loosing detail. Experimentation is encouraged. + +

References:

+ +

The alpha-trimmed mean filter is based on the description in IEEE +CG&A May 1990 Page 23 by Mark E. Lee and Richard A. Redner, and +has been enhanced to allow continuous alpha adjustment. + +

The optimal estimation filter is taken from an article +"Converting Dithered Images Back to Gray Scale" by Allen +Stenger, Dr Dobb's Journal, November 1992, and this article references +"Digital Image Enhancement and Noise Filtering by Use of Local +Statistics", Jong-Sen Lee, IEEE Transactions on Pattern Analysis +and Machine Intelligence, March 1980. + +

The edge enhancement details are from pgmenhance, which is taken from Philip +R. Thompson's "xim" program, which in turn took it from +section 6 of "Digital Halftones by Dot Diffusion", +D. E. Knuth, ACM Transaction on Graphics Vol. 6, No. 4, October 1987, +which in turn got it from two 1976 papers by J. F. Jarvis et. al. + +

+ +

The parameters are: + +

alpha +: The alpha value (described above), in decimal. May be fractional. + +
radius +: The radius (described above), in decimal. May be fractional. +

+ +

AUTHOR

+ +Graeme W. Gill graeme@labtam.oz.au + +

SYNOPSIS +
DESCRIPTION +
+
References: +
SEE ALSO +
AUTHOR +

+ + -- cgit 1.4.1