eliminate_min_max — Smooth an image in the spatial domain to suppress noise.
eliminate_min_max(Image : FilteredImage : MaskWidth, MaskHeight, Gap, Mode : )
eliminate_min_max smooths an image by replacing gray values with
neighboring mean values, or local minima/maxima. In order to prevent
edges and lines from being smoothed, only those gray values that represent
local minima or maxima are replaced (if there is a line or edge within an
image there will be at least one neighboring pixel with a comparable
gray value). Gap controls the strictness of replacement: Only
gray values that exceed all other values within their local neighborhood
more than Gap and all values that fall below their neighboring
more than Gap are replaced:
E(x,y) represents a NxM sized rectangular
neighborhood of an pixel at position (x,y),
containing all pixels within the neighborhood except the pixel itself;
if gray_value(x,y) >= Gap +
maximum(E(x,y)) then replacement;
else if gray_value(x,y) + Gap <=
minimum(E(x,y)) then replacement;
else adopt gray_value(x,y) without change;
Mode specifies how to perform the new value in case of a
replacement.
Mode = 1 --> replace a local maximum with next
minor local
maximum and replace a local minimum with next
bigger local minimum
Mode = 2 --> replace with mean value of all
pixels within the local neighborhood
(including the replaced pixel)
Mode = 3 --> replace with median value of all
pixels within the
local neighborhood (including the replaced pixel
(this is default and used if Mode has got
any other value than 1 or 2)
MaskWidth and MaskHeight specify the width and height of
the rectangular neighborhood.
Border treatment: Pixels outside the image border are not considered (e.g.:
With a local 3x3-mask the neighborhood of a pixel at
(0,0) reduces to the pixels at (1,0),(0,1)
and (1,1)).
For an explanation of the concept of smoothing filters see the introduction of chapter Filters / Smoothing.
eliminate_min_max only can work on byte images (HALCON image
type BYTE_IMAGE). If MaskWidth or MaskHeight is an even
number, it is replaced by the next higher odd number (this allows the
unique extraction of the center of the filter mask).
Width/height of the mask may not exceed the image width/height.
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 / uint2)
Image to smooth.
FilteredImage (output_object) (multichannel-)image(-array) → object (byte / uint2)
Smoothed image.
MaskWidth (input_control) extent.x → (integer)
Width of filter mask.
Default value: 3
Suggested values: 3, 5, 7, 9
Typical range of values: 3
≤
MaskWidth
≤
width(Image)
Minimum increment: 2
Recommended increment: 2
Restriction: odd(MaskWidth)
MaskHeight (input_control) extent.y → (integer)
Height of filter mask.
Default value: 3
Suggested values: 3, 5, 7, 9
Typical range of values: 3
≤
MaskHeight
≤
width(Image)
Minimum increment: 2
Recommended increment: 2
Restriction: odd(MaskWidth)
Gap (input_control) number → (real)
Gap between local maximum/minimum and all other gray values of the neighborhood.
Default value: 1.0
Suggested values: 1.0, 2.0, 5.0, 10.0
Mode (input_control) integer → (integer)
Replacement rule (1 = next minimum/maximum, 2 = average, 3 =median).
Default value: 3
List of values: 1, 2, 3
eliminate_min_max returns TRUE if all parameters are
correct. If the input is empty eliminate_min_max returns with
an error message.
wiener_filter,
wiener_filter_ni
mean_sp,
mean_image,
median_image,
median_weighted,
binomial_filter,
gauss_filter,
smooth_image
M. Imme:“A Noise Peak Elimination Filter”; S. 204-211 in CVGIP
Graphical Models and Image Processing, Vol. 53, No. 2, March 1991
M. Lückenhaus:“Grundlagen des Wiener-Filters und seine Anwendung in der
Bildanalyse”; Diplomarbeit;
Technische Universität München, Institut für Informatik;
Lehrstuhl Prof. Radig; 1995.
Foundation