diff options
Diffstat (limited to 'upstream/opensuse-leap-15-6/man1/pnmnlfilt.1')
| -rw-r--r-- | upstream/opensuse-leap-15-6/man1/pnmnlfilt.1 | 179 |
1 files changed, 179 insertions, 0 deletions
diff --git a/upstream/opensuse-leap-15-6/man1/pnmnlfilt.1 b/upstream/opensuse-leap-15-6/man1/pnmnlfilt.1 new file mode 100644 index 00000000..27979a68 --- /dev/null +++ b/upstream/opensuse-leap-15-6/man1/pnmnlfilt.1 @@ -0,0 +1,179 @@ +\ +.\" This man page was generated by the Netpbm tool 'makeman' from HTML source. +.\" Do not hand-hack it! If you have bug fixes or improvements, please find +.\" the corresponding HTML page on the Netpbm website, generate a patch +.\" against that, and send it to the Netpbm maintainer. +.TH "Pnmnlfilt User Manual" 0 "24 October 2006" "netpbm documentation" + +.SH NAME + +pnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal +estimation smoothing, edge enhancement. + +.UN synopsis +.SH SYNOPSIS + +\fBpnmnlfilt\fP +\fIalpha\fP +\fIradius\fP +[\fIpnmfile\fP] + +.UN description +.SH DESCRIPTION +.PP +This program is part of +.BR "Netpbm" (1)\c +\&. +.PP +\fBpnmnlfilt\fP produces an output image where the pixels are a +summary of multiple pixels near the corresponding location in an input +image. +.PP +This program works on multi-image streams. +.PP +This is something of a swiss army knife filter. It has 3 distinct +operating modes. In all of the modes \fBpnmnlfilt\fP 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), \fBpnmnlfilt\fP 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. +.PP +Your choice of "alpha" parameter selects among the three +modes. + +.UN alphatrimmedmean +.SS +Alpha trimmed mean filter (0.0 <= alpha <= 0.5) +.PP +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 +.PP +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 + +.UN optimalestsmooth +.SS +Optimal estimation smoothing. (1.0 <= alpha <= 2.0) +.PP +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. + +.UN edgeenhance +.SS Edge enhancement. (-0.1 >= alpha >= -0.9) +.PP +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 + +.UN combination +.SS Combination use. +.PP +The various modes of \fBpnmnlfilt\fP 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. +.PP +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 losing detail. Experimentation is encouraged. + +.UN references +.SH References: +.PP +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. +.PP +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. +.PP +The edge enhancement details are from +.BR "pgmenhance" (1)\c +\&, 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. + +.UN parameters +.SH +.PP +The parameters are: + + +.TP +\fIalpha\fP +The alpha value (described above), in decimal. May be fractional. + +.TP +\fIradius\fP +The radius (described above), in decimal. May be fractional. + + +.UN seealso +.SH SEE ALSO +.BR "pgmenhance" (1)\c +\&, +.BR "pnmconvol" (1)\c +\&, +.BR "pnm" (5)\c +\& + +.UN author +.SH AUTHOR + +Graeme W. Gill \fIgraeme@labtam.oz.au\fP +.SH DOCUMENT SOURCE +This manual page was generated by the Netpbm tool 'makeman' from HTML +source. The master documentation is at +.IP +.B http://netpbm.sourceforge.net/doc/pnmnlfilt.html +.PP
\ No newline at end of file |