Deblur image using blind deconvolution
[
deconvolves image J
,psfr
] = deconvblind(I
,psfi
)I
using the maximum likelihood algorithm and
an initial estimate of the point-spread function (PSF), psfi
.
The deconvblind
function returns both the deblurred image
J
and a restored PSF, psfr
.
To improve the restoration, deconvblind
supports several
optional parameters, described below. Use []
as a placeholder if
you do not specify an intermediate parameter.
[
specifies which pixels in the input image J
,psfr
] = deconvblind(I
,psfi
,iter
,dampar
,weight
)I
are considered in the
restoration. The value of an element in the weight
array
determines how much the pixel at the corresponding position in the input image is
considered. For example, to exclude a pixel from consideration, assign it a value of
0
in the weight
array. You can adjust
the weight value assigned to each pixel according to the amount of flat-field
correction.
[
,
where J
,psfr
] = deconvblind(___,fun
)fun
is a handle to a function that describes additional
constraints on the PSF. fun
is called at the end of each
iteration. For more information about function handles, see Create Function Handle.
You can use deconvblind
to perform a deconvolution that
starts where a previous deconvolution stopped. To use this feature, pass the
input image I
and the initial guess at the PSF,
psfi
, as cell arrays: {I}
and
{psfi}
. When you do, the deconvblind
function returns the output image J
and the restored
point-spread function, psfr
, as cell arrays, which can then
be passed as the input arrays into the next deconvblind
call.
The output cell array J
contains four elements:
J{1}
contains I
, the original
image.
J{2}
contains the result of the last iteration.
J{3}
contains the result of the next-to-last
iteration.
J{4}
is an array generated by the iterative
algorithm.
The output image J
could exhibit ringing introduced by
the discrete Fourier transform used in the algorithm. To reduce the ringing, use
I = edgetaper(I,psfi)
before calling
deconvblind
.
[1] D.S.C. Biggs and M. Andrews, Acceleration of iterative image restoration algorithms, Applied Optics, Vol. 36, No. 8, 1997.
[2] R.J. Hanisch, R.L. White, and R.L. Gilliland, Deconvolutions of Hubble Space Telescope Images and Spectra, Deconvolution of Images and Spectra, Ed. P.A. Jansson, 2nd ed., Academic Press, CA, 1997.
[3] Timothy J. Holmes, et al, Light Microscopic Images Reconstructed by Maximum Likelihood Deconvolution, Handbook of Biological Confocal Microscopy, Ed. James B. Pawley, Plenum Press, New York, 1995.