Pnmnlfilt UserManual(0)Pnmnlfilt UserManual(0)

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

pnmnlfiltalpharadius[pnmfile]

This 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 oper- ating 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 the 9 pixels in a 3x3 block, 7 hexagonal area samples are taken, the size of the hexagons being controlled by the radius parame- ter. A radius value of 0.3333 means that the 7 hexagons exactly fit into the center pixel (ie. there will be no filtering effect). A radius value of 1.0 means that the 7 hexagons exactly fit a 3x3 pixel array.

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

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 propor- tional 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.

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

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 loosing detail. Experimentation is encouraged.

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 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),whichistaken-fromPhilipR. Thompson’s ’xim’ program, which in turn took it from sec- tion 6 of ’Digital Halftones by Dot Diffusion’, D. E. Knuth, ACM Trans- action on Graphics Vol. 6, No. 4, October 1987, which in turn got it from two 1976 papers by J. F. Jarvis et. al.

pgmenhance(1),pnmconvol(1),pnm(1)

Graeme W. Gillgraeme@labtam.oz.aunetpbm documentation 5 February 1993 Pnmnlfilt UserManual(0)

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