mean_image — Smooth by averaging.
mean_image(Image : ImageMean : MaskWidth, MaskHeight : )
The operator mean_image carries out a linear smoothing with
the gray values of all input images (Image). The filter
matrix consists of ones (evaluated equally) and has the size
MaskHeight x MaskWidth. The result of the convolution is divided
by MaskHeight x MaskWidth. For border treatment the gray values are
reflected at the image edges.
For mean_image special optimizations are implemented that use SIMD
technology. The actual application of these special optimizations is
controlled by the system parameter 'mmx_enable'
(see set_system).
If 'mmx_enable' is set to 'true' (and the SIMD
instruction set is available), the internal calculations are performed
using SIMD technology.
Note that SIMD technology performs best on large, compact input regions.
Depending on the input region and the capabilities of the hardware the
execution of mean_image might even take significantly
more time with SIMD technology than without.
At any rate, it is advantageous for the performance of mean_image
to choose the input region of Image such that any border treatment
is avoided.
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
If even values instead of odd values are given for
MaskHeight or MaskWidth, the routine uses the
next larger odd values instead (this way the center of the filter
mask is always explicitly determined).
mean_image can be executed on OpenCL devices for byte, int2, uint2,
int4 and real images if MaskHeight is less than twice the height
of Image. For OpenCL, the mean filter value is calculated
internally using either 32 bit signed integers (for all integer image
types) or single precision floating point (for real images). This can lead
to overflows (and thus incorrect results) if Image is either an
int4 or real image and the full dynamic range is used. Additionally, to
improve performance a full scan of each row of Image is calculated
(again using either 32 bit integer or single precision floating point
arithmetic) if MaskWidth is bigger than 9. This can also lead to
overflows with very wide images even for byte, int2, or uint2 images. In
these cases, the CPU version of mean_image should be used.
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Image (input_object) (multichannel-)image(-array) → object (byte* / int2* / uint2* / int4* / int8 / real* / vector_field) *allowed for compute devices
Image to be smoothed.
ImageMean (output_object) (multichannel-)image(-array) → object (byte / int2 / uint2 / int4 / int8 / real / vector_field)
Smoothed image.
MaskWidth (input_control) extent.x → (integer)
Width of filter mask.
Default value: 9
Suggested values: 3, 5, 7, 9, 11, 15, 23, 31, 43, 61, 101
Typical range of values: 1
≤
MaskWidth
≤
501
Minimum increment: 2
Recommended increment: 2
Restriction: odd(MaskWidth) && MaskWidth < width(Image) * 2
MaskHeight (input_control) extent.y → (integer)
Height of filter mask.
Default value: 9
Suggested values: 3, 5, 7, 9, 11, 15, 23, 31, 43, 61, 101
Typical range of values: 1
≤
MaskHeight
≤
501
Minimum increment: 2
Recommended increment: 2
Restriction: odd(MaskHeight) && MaskHeight < height(Image) * 2
read_image(Image,'fabrik') mean_image(Image,Mean,3,3) dev_display(Mean)
For each pixel: O(15).
If the parameter values are correct the operator mean_image
returns the value TRUE. The behavior in case of empty input (no
input images available) is set via the operator
set_system('no_object_result',<Result>).
If necessary an exception is raised.
reduce_domain,
rectangle1_domain
binomial_filter,
gauss_filter,
smooth_image
anisotropic_diffusion,
sigma_image,
convol_image,
gen_lowpass
Foundation