filters(9) [plan9 man page]
FILTERS(9.1) FILTERS(9.1) NAME
adapt, ahe, crispen, laplace, edge, edge2, edge3, extremum, median, nonoise, smooth, shadepic - image neighborhood operators SYNOPSIS
fb/adapt [ input ] fb/ahe [ input ] fb/crispen [ input ] fb/laplace [ input ] fb/edge [ input ] fb/edge2 [ input ] fb/edge3 [ input ] fb/extremum [ input ] fb/median [ input ] fb/nonoise [ input ] fb/smooth [ input ] fb/shadepic [ -lx y z ] [ input ] DESCRIPTION
Gathered here are descriptions of programs that compute the pixels of an output image by performing some operation on a neighborhood of each pixel of their input image (default standard input). Each program writes the output image on standard output. The programs process multi-channel inputs by treating each channel independently. Adapt performs adaptive contrast enhancement by examining the 7x7 region centered on each input pixel, remapping the center pixel linearly in a way that would send the neighborhood's maximum value to 255 and its minimum to 0. To avoid divide checks, no mapping is done if all pixels in the region have the same value. Ahe performs adaptive histogram equalization by examining the 17x17 region centered on each input pixel, counting the number of pixels whose value is less than the center pixel. (It counts 1/2 for each pixel equal to the center value.) Output pixel values are 255 times the count divided by the window size. Crispen examines the 3x3 region surrounding each input pixel, computing 9 times the center pixel minus the sum of its eight neighbors. This is a fairly extreme high-pass filter and sharpens edges substantially. Laplace computes 5 times the center pixel minus the sum of its four vertical and horizontal neighbors. This adds a 3x3 discrete Laplacian to the original image, and is a less extreme high-pass filter than crispen. Edge, edge2, and edge3 detect edges in various ways. Edge examines the 3x3 region surrounding each input pixel, outputting 8 times the center value minus the sum of its eight neighbors. Edge2 applies a Sobel operator to the input image. It approximates the image's gradient by finite differences on a 3x3 neighborhood, out- putting the vector length of the gradient approximation. Edge3 likewise approximates the gradient of the input image. The output is roughly the phase angle of the gradient approximation, scaled between 0 and 255. Extremum examines the 3x3 region surrounding each input pixel, outputting the value that differs most from the center value. In case of a tie, the larger candidate is chosen. Median does noise reduction by replacing each pixel of the input image by the median of the 3x3 region surrounding it. Nonoise implements the Bayer-Powell noise reduction filter. It computes the average value of the eight neighbors of each pixel of the input image, and substitutes it for the pixel value if the two differ by more than 64. Smooth low-pass filters its input image by convolution with a Bartlett window. Shadepic treats its input image as an array of elevations. At each pixel it approximates the normal vector to the height-field by finite differences on a 3x3 neighborhood and outputs 255 times its dot product with the unit vector in the light-source direction specified by option -l (default 1,-1,1). If the dot product is negative, it is clamped at zero. (This computation is just Lambertian diffuse reflec- tion.) SOURCE
/sys/src/fb SEE ALSO
picfile(9.6) BUGS
There are too many weird wired-in sizes. FILTERS(9.1)
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pnmnlfilt(1) General Commands Manual pnmnlfilt(1) NAME
pnmnlfilt - non-linear filters: smooth, alpha trim mean, optimal estimation smoothing, edge enhancement. SYNOPSIS
pnmnlfilt alpha radius [pnmfile] DESCRIPTION
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 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 parameter. 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. 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 nor- mal 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 enhance- ment, 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 estima- tion 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 fil- ter. 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(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. SEE ALSO
pgmenhance(1), pnmconvol(1), pnm(5) BUGS
Integers and tables may overflow if PPM_MAXMAXVAL is greater than 255. AUTHOR
Graeme W. Gill graeme@labtam.oz.au 5 February 1993 pnmnlfilt(1)