Deblur image using blind deconvolution
[J,PSF] = deconvblind(I, INITPSF)
[J,PSF] = deconvblind(I, INITPSF, NUMIT)
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR)
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR, WEIGHT)
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR, WEIGHT, READOUT)
[J,PSF] = deconvblind(..., FUN, P1,
P2,...,PN)
[J,PSF] = deconvblind(I, INITPSF)
deconvolves
image I
using the maximum likelihood algorithm,
returning both the deblurred image J
and a restored
point-spread function PSF
. The restored PSF
is
a positive array that is the same size as INITPSF
,
normalized so its sum adds up to 1. The PSF
restoration
is affected strongly by the size of the initial guess INITPSF
and
less by the values it contains. For this reason, specify an array
of 1's as your INITPSF
.
I
can be an N-dimensional array.
To improve the restoration, deconvblind
supports
several optional parameters, described below. Use []
as
a placeholder if you do not specify an intermediate parameter.
[J,PSF] = deconvblind(I, INITPSF, NUMIT)
specifies
the number of iterations (default is 10).
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR)
specifies the threshold deviation of the resulting
image from the input image I
(in terms of the standard
deviation of Poisson noise) below which damping occurs. The iterations
are suppressed for the pixels that deviate within the DAMPAR
value
from their original value. This suppresses the noise generation in
such pixels, preserving necessary image details elsewhere. The default
value is 0
(no damping).
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR, WEIGHT)
specifies which pixels in the input image I
are
considered in the restoration. By default, WEIGHT
is
a unit array, the same size as the input image. You can assign a value
between 0.0 and 1.0 to elements in the WEIGHT
array.
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.
[J,PSF] = deconvblind(I, INITPSF, NUMIT,
DAMPAR, WEIGHT, READOUT)
, where READOUT
is
an array (or a value) corresponding to the additive noise (e.g., background,
foreground noise) and the variance of the read-out camera noise. READOUT
has
to be in the units of the image. The default value is 0
.
[J,PSF] = deconvblind(..., FUN, P1,
P2,...,PN)
, where FUN
is a function
describing additional constraints on the PSF. FUN
must
be a function handle.
FUN
is called at the end of each iteration. FUN
must
accept the PSF
as its first argument and can accept
additional parameters P1
, P2
,...
, PN
.
The FUN
function should return one argument, PSF
,
that is the same size as the original PSF and that satisfies the positivity
and normalization constraints. The function colfilt
zero-pads A
,
if necessary.
Note
The output image |
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, INITPSF
, as cell arrays: {I}
and {INITPSF}
.
When you do, the deconvblind
function returns the
output image J
and the restored point-spread function, PSF
,
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.
I
and INITPSF
can be uint8
, uint16
, int16
, single
,
or double
. DAMPAR
and READOUT
must
have the same class as the input image. Other inputs have to be double
.
The output image J
(or the first array of the output
cell) has the same class as the input image I
.
The output PSF
is double
.