simulate_defocus — Simulate an uniform out-of-focus blurring of an image.
simulate_defocus(Image : DefocusedImage : Blurring : )
simulate_defocus simulates out-of-focus blurring of an image.
All parts of the image are blurred uniformly.
Blurring specifies the extent of blurring by defining
the “blur radius” (out-of-focus blurring maps each image pixel on a small
circle with a radius of Blurring - specified in “number of
pixels”). If specified less than null, the absolute value of
Blurring is used. Simulation of blurring is done by a
convolution of the image with a blurring specific impulse response.
The convolution is realized by multiplication in the Fourier domain.
Image (input_object) (multichannel-)image(-array) → object (byte / direction / cyclic / int1 / int2 / uint2 / int4 / real)
Image to blur.
DefocusedImage (output_object) image(-array) → object (real)
Blurred image.
Blurring (input_control) real → (real)
Degree of blurring.
Default value: 5.0
Suggested values: 1.0, 5.0, 10.0, 15.0, 18.0
simulate_defocus returns 2 (H_MSG_TRUE) if all parameters are
correct. If the input is empty simulate_defocus returns with
an error message.
gen_psf_defocus,
simulate_motion,
gen_psf_motion
wiener_filter,
wiener_filter_ni
gen_psf_defocus,
simulate_motion,
gen_psf_motion
Reginald L. Lagendijk, Jan Biemond: Iterative Identification and Restoration
of Images, Kluwer Academic Publishers Boston/Dordrecht/London, 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