# NAME

Image::Leptonica::Func::bilateral

# VERSION

version 0.04

`bilateral.c`

```
bilateral.c
Top level approximate separable grayscale or color bilateral filtering
PIX *pixBilateral()
PIX *pixBilateralGray()
Implementation of approximate separable bilateral filter
static L_BILATERAL *bilateralCreate()
static void *bilateralDestroy()
static PIX *bilateralApply()
Slow, exact implementation of grayscale or color bilateral filtering
PIX *pixBilateralExact()
PIX *pixBilateralGrayExact()
PIX *pixBlockBilateralExact()
Kernel helper function
L_KERNEL *makeRangeKernel()
This includes both a slow, exact implementation of the bilateral
filter algorithm (given by Sylvain Paris and Frédo Durand),
and a fast, approximate and separable implementation (following
Yang, Tan and Ahuja). See bilateral.h for algorithmic details.
The bilateral filter has the nice property of applying a gaussian
filter to smooth parts of the image that don't vary too quickly,
while at the same time preserving edges. The filter is nonlinear
and cannot be decomposed into two separable filters; however,
there exists an approximate method that is separable. To further
speed up the separable implementation, you can generate the
intermediate data at reduced resolution.
The full kernel is composed of two parts: a spatial gaussian filter
and a nonlinear "range" filter that depends on the intensity difference
between the reference pixel at the spatial kernel origin and any other
pixel within the kernel support.
In our implementations, the range filter is a parameterized,
one-sided, 256-element, monotonically decreasing gaussian function
of the absolute value of the difference between pixel values; namely,
abs(I2 - I1). In general, any decreasing function can be used,
and more generally, any two-dimensional kernel can be used if
you wish to relax the 'abs' condition. (In that case, the range
filter can be 256 x 256).
```

# FUNCTIONS

## makeRangeKernel

L_KERNEL * makeRangeKernel ( l_float32 range_stdev )

```
makeRangeKernel()
Input: range_stdev (> 0)
Return: kel, or null on error
Notes:
(1) Creates a one-sided Gaussian kernel with the given
standard deviation. At grayscale difference of one stdev,
the kernel falls to 0.6, and to 0.01 at three stdev.
(2) A typical input number might be 20. Then pixels whose
value differs by 60 from the center pixel have their
weight in the convolution reduced by a factor of about 0.01.
```

## pixBilateral

PIX * pixBilateral ( PIX *pixs, l_float32 spatial_stdev, l_float32 range_stdev, l_int32 ncomps, l_int32 reduction )

```
pixBilateral()
Input: pixs (8 bpp gray or 32 bpp rgb, no colormap)
spatial_stdev (of gaussian kernel; in pixels, > 0.5)
range_stdev (of gaussian range kernel; > 5.0; typ. 50.0)
ncomps (number of intermediate sums J(k,x); in [4 ... 30])
reduction (1, 2 or 4)
Return: pixd (bilateral filtered image), or null on error
Notes:
(1) This performs a relatively fast, separable bilateral
filtering operation. The time is proportional to ncomps
and varies inversely approximately as the cube of the
reduction factor. See bilateral.h for algorithm details.
(2) We impose minimum values for range_stdev and ncomps to
avoid nasty artifacts when either are too small. We also
impose a constraint on their product:
ncomps * range_stdev >= 100.
So for values of range_stdev >= 25, ncomps can be as small as 4.
Here is a qualitative, intuitive explanation for this constraint.
Call the difference in k values between the J(k) == 'delta', where
'delta' ~ 200 / ncomps
Then this constraint is roughly equivalent to the condition:
'delta' < 2 * range_stdev
Note that at an intensity difference of (2 * range_stdev), the
range part of the kernel reduces the effect by the factor 0.14.
This constraint requires that we have a sufficient number of
PCBs (i.e, a small enough 'delta'), so that for any value of
image intensity I, there exists a k (and a PCB, J(k), such that
|I - k| < range_stdev
Any fewer PCBs and we don't have enough to support this condition.
(3) The upper limit of 30 on ncomps is imposed because the
gain in accuracy is not worth the extra computation.
(4) The size of the gaussian kernel is twice the spatial_stdev
on each side of the origin. The minimum value of
spatial_stdev, 0.5, is required to have a finite sized
spatial kernel. In practice, a much larger value is used.
(5) Computation of the intermediate images goes inversely
as the cube of the reduction factor. If you can use a
reduction of 2 or 4, it is well-advised.
(6) The range kernel is defined over the absolute value of pixel
grayscale differences, and hence must have size 256 x 1.
Values in the array represent the multiplying weight
depending on the absolute gray value difference between
the source pixel and the neighboring pixel, and should
be monotonically decreasing.
(7) Interesting observation. Run this on prog/fish24.jpg, with
range_stdev = 60, ncomps = 6, and spatial_dev = {10, 30, 50}.
As spatial_dev gets larger, we get the counter-intuitive
result that the body of the red fish becomes less blurry.
```

## pixBilateralExact

PIX * pixBilateralExact ( PIX *pixs, L_KERNEL *spatial_kel, L_KERNEL *range_kel )

```
pixBilateralExact()
Input: pixs (8 bpp gray or 32 bpp rgb)
spatial_kel (gaussian kernel)
range_kel (<optional> 256 x 1, monotonically decreasing)
Return: pixd (8 bpp bilateral filtered image)
Notes:
(1) The spatial_kel is a conventional smoothing kernel, typically a
2-d Gaussian kernel or other block kernel. It can be either
normalized or not, but must be everywhere positive.
(2) The range_kel is defined over the absolute value of pixel
grayscale differences, and hence must have size 256 x 1.
Values in the array represent the multiplying weight for each
gray value difference between the target pixel and center of the
kernel, and should be monotonically decreasing.
(3) If range_kel == NULL, a constant weight is applied regardless
of the range value difference. This degenerates to a regular
pixConvolve() with a normalized kernel.
```

## pixBilateralGray

PIX * pixBilateralGray ( PIX *pixs, l_float32 spatial_stdev, l_float32 range_stdev, l_int32 ncomps, l_int32 reduction )

```
pixBilateralGray()
Input: pixs (8 bpp gray)
spatial_stdev (of gaussian kernel; in pixels, > 0.5)
range_stdev (of gaussian range kernel; > 5.0; typ. 50.0)
ncomps (number of intermediate sums J(k,x); in [4 ... 30])
reduction (1, 2 or 4)
Return: pixd (8 bpp bilateral filtered image), or null on error
Notes:
(1) See pixBilateral() for constraints on the input parameters.
(2) See pixBilateral() for algorithm details.
```

## pixBilateralGrayExact

PIX * pixBilateralGrayExact ( PIX *pixs, L_KERNEL *spatial_kel, L_KERNEL *range_kel )

```
pixBilateralGrayExact()
Input: pixs (8 bpp gray)
spatial_kel (gaussian kernel)
range_kel (<optional> 256 x 1, monotonically decreasing)
Return: pixd (8 bpp bilateral filtered image)
Notes:
(1) See pixBilateralExact().
```

## pixBlockBilateralExact

PIX* pixBlockBilateralExact ( PIX *pixs, l_float32 spatial_stdev, l_float32 range_stdev )

```
pixBlockBilateralExact()
Input: pixs (8 bpp gray or 32 bpp rgb)
spatial_stdev (> 0.0)
range_stdev (> 0.0)
Return: pixd (8 bpp or 32 bpp bilateral filtered image)
Notes:
(1) See pixBilateralExact(). This provides an interface using
the standard deviations of the spatial and range filters.
(2) The convolution window halfwidth is 2 * spatial_stdev,
and the square filter size is 4 * spatial_stdev + 1.
The kernel captures 95% of total energy. This is compensated
by normalization.
(3) The range_stdev is analogous to spatial_halfwidth in the
grayscale domain [0...255], and determines how much damping of the
smoothing operation is applied across edges. The larger this
value is, the smaller the damping. The smaller the value, the
more edge details are preserved. These approximations are useful
for deciding the appropriate cutoff.
kernel[1 * stdev] ~= 0.6 * kernel[0]
kernel[2 * stdev] ~= 0.14 * kernel[0]
kernel[3 * stdev] ~= 0.01 * kernel[0]
If range_stdev is infinite there is no damping, and this
becomes a conventional gaussian smoothing.
This value does not affect the run time.
(4) If range_stdev is negative or zero, the range kernel is
ignored and this degenerates to a straight gaussian convolution.
(5) This is very slow for large spatial filters. The time
on a 3GHz pentium is roughly
T = 1.2 * 10^-8 * (A * sh^2) sec
where A = # of pixels, sh = spatial halfwidth of filter.
```

# AUTHOR

Zakariyya Mughal <zmughal@cpan.org>

# COPYRIGHT AND LICENSE

This software is copyright (c) 2014 by Zakariyya Mughal.

This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.