A Survey of Gaussian Convolution Algorithms
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A fast low-accuracy approximation of Gaussian convolution. More...
A fast low-accuracy approximation of Gaussian convolution.
This code implements the basic iterated box filter approximation of Gaussian convolution as developed by Wells. This approach is based on the efficient recursive implementation of the box filter as
where r is the box radius.
While box filtering is very efficient, it has the limitation that only a quantized set of values can be approximated because the box radius r is integer. Extended box filter Gaussian convolution and Stacked integral images Gaussian convolution are extensions of box filtering that allow
to vary continuously.
Functions | |
static void | box_filter (num *dest, long dest_stride, const num *src, long src_stride, long N, long r) |
Perform one pass of box filtering. More... | |
void | box_gaussian_conv (num *dest, num *buffer, const num *src, long N, long stride, num sigma, int K) |
Box filtering approximation of Gaussian convolution. More... | |
void | box_gaussian_conv_image (num *dest, num *buffer, const num *src, int width, int height, int num_channels, num sigma, int K) |
Box filtering approximation of 2D Gaussian convolution. More... | |
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static |
Perform one pass of box filtering.
dest | destination array |
dest_stride | stride between successive samples of dest |
src_stride | src array (must be distinct from dest) |
src_stride | stride between successive samples of src |
N | number of samples |
r | radius of the box filter |
Performs one pass of box filtering with radius r (diameter 2r+1) according to the recursive filter
Definition at line 43 of file gaussian_conv_box.c.
void box_gaussian_conv | ( | num * | dest_data, |
num * | buffer_data, | ||
const num * | src, | ||
long | N, | ||
long | stride, | ||
num | sigma, | ||
int | K | ||
) |
Box filtering approximation of Gaussian convolution.
dest_data | destination array |
buffer_data | array with space for at least N samples |
src | input, overwritten if src = dest_data |
N | number of samples |
stride | stride between successive samples |
sigma | Gaussian standard deviation in pixels |
K | number of box filter passes |
sigma
is rounded to the closest supported value.This routine performs iterated box filtering approximation of Gaussian convolution. Well's approximation is used to select the box filter radius.
The filtering itself is performed by calling the box_filter() function K
times. Since box_filter() requires that the source and destination arrays are distinct, the iteration alternates the roles of the dest and buffer arrays. For example the iteration pseudocode for K
= 4 is
buffer <- box_filter(src) dest <- box_filter(buffer) buffer <- box_filter(dest) dest <- box_filter(buffer) dest <- dest * scale
The convolution can be performed in-place by setting src
= dest
(the source array is overwritten with the result). However, buffer_data
must be distinct from dest_data
.
Definition at line 105 of file gaussian_conv_box.c.
void box_gaussian_conv_image | ( | num * | dest, |
num * | buffer, | ||
const num * | src, | ||
int | width, | ||
int | height, | ||
int | num_channels, | ||
num | sigma, | ||
int | K | ||
) |
Box filtering approximation of 2D Gaussian convolution.
dest | destination image |
buffer | array with at least max(width,height) samples |
src | input, overwritten if src = dest |
width | image width |
height | image height |
num_channels | number of image channels |
sigma | Gaussian standard deviation in pixels |
K | number of box filter passes |
Similar to box_gaussian_conv(), this routine approximates 2D Gaussian convolution with box filtering.
The convolution can be performed in-place by setting src
= dest
(the source array is overwritten with the result). However, buffer
must be distinct from dest
.
Definition at line 195 of file gaussian_conv_box.c.