emphasize

emphasize ( Image : ImageEmphasize : MaskWidth, MaskHeight, Factor : )

Enhance contrast of the image.

The operator emphasize emphasizes high frequency areas of the image (edges and corners). The resulting images appears sharper.

First the procedure carries out a filtering with the low pass (mean_image). The resulting gray values (res) are calculated from the obtained gray values (mean) and the original gray values (orig) as follows: res := round((orig - mean) * Factor) + orig

Parameters

`Image` (input_object)	(multichannel-)image(-array) `->` object : byte / int2 / uint2
Image to be enhanced.

`ImageEmphasize` (output_object)	(multichannel-)image(-array) `->` object : byte / int2 / uint2
contrast enhanced image.

`MaskWidth` (input_control)	extent.x `->` integer
Width of low pass mask.
Default value: 7
Suggested values: 3, 5, 7, 9, 11, 15, 21, 25, 31, 39
Typical range of values: `3 <= MaskWidth <= 201`
Minimum increment: 2
Recommended increment: 2

`MaskHeight` (input_control)	extent.y `->` integer
Height of the low pass mask.
Default value: 7
Suggested values: 3, 5, 7, 9, 11, 15, 21, 25, 31, 39
Typical range of values: `3 <= MaskHeight <= 201`
Minimum increment: 2
Recommended increment: 2

`Factor` (input_control)	real `->` real
Intensity of contrast emphasis.
Default value: 1.0
Suggested values: 0.3, 0.5, 0.7, 1.0, 1.4, 1.8, 2.0
Typical range of values: `0.0 <= Factor <= 20.0` (sqrt)
Minimum increment: 0.01
Recommended increment: 0.2
Restriction: `(0 < Factor) && (Factor < 20)`

Example

read_image(Image,'mreut') 
disp_image(Image,WindowHandle) 
draw_region(Region,WindowHandle) 
reduce_domain(Image,Region,Mask) 
emphasize(Mask,Sharp,7,7,2.0) 
disp_image(Sharp,WindowHandle).

Result

If the parameter values are correct the operator emphasize returns the value 2 (H_MSG_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 handling is raised.

Parallelization Information

emphasize is reentrant and automatically parallelized (on tuple level, channel level, domain level).

Possible Successors

disp_image

Alternatives

mean_image, sub_image, laplace, add_image

See also

mean_image, highpass_image

Module

Foundation