# FreeBSD Manual Pages

Pnmnlfilt User Manual(0) Pnmnlfilt User Manual(0)NAMEpnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal esti- mation smoothing, edge enhancement.SYNOPSISpnmnlfiltalpharadius[pnmfile]DESCRIPTIONThis program is part ofNetpbm(1).pnmnlfiltproduces 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 op- erating modes. In all of the modespnmnlfiltexamines each pixel in the image and processes it according to the values of it and its sur- rounding pixels. Rather than using a square block of surrounding pix- els (e.g. the subject pixel and its 8 immediate neighbors, in a 3x3 square),pnmnlfiltuses 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.Alphatrimmedmeanfilter(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 de- termine 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 ef- fect, 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 = 1.0Optimalestimationsmoothing.(1.0<=alpha<=2.0)This type of filter applies a smoothing filter adaptively over the im- age. 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 ef- fective 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.Edgeenhancement.(-0.1>=alpha>=-0.9)This is the opposite type of filter to the smoothing filter. It en- hances edges. The alpha parameter controls the amount of edge enhance- ment, from subtle (-0.1) to blatant (-0.9). The radius parameter con- trols the effective radius as usual, but useful values are between 0.5 and 0.9. Try starting with values of alpha = 0.3, radius = 0.8Combinationuse.The various modes ofpnmnlfiltcan 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. Sev- eral passes of the optimal estimation filter with declining alpha val- ues are more effective than a single pass with a large alpha value. As usual, there is a tradeoff between filtering effectiveness and losing 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 en- hanced 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 Jour- nal, November 1992, and this article references "Digital Image Enhance- ment 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 frompgmenhance(1), 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:alphaThe alpha value (described above), in decimal. May be frac- tional.radiusThe radius (described above), in decimal. May be fractional.SEE ALSOpgmenhance(1),pnmconvol(1),pnm(5)AUTHORGraeme W. Gillgraeme@labtam.oz.auDOCUMENT SOURCEThis manual page was generated by the Netpbm tool 'makeman' from HTML source. The master documentation is athttp://netpbm.sourceforge.net/doc/pnmnlfilt.htmlnetpbm documentation 24 October 2006 Pnmnlfilt User Manual(0)

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