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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.

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,PSF)`

before
calling `deconvblind`

.

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`

.

[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.

`deconvlucy`

| `deconvreg`

| `deconvwnr`

| `edgetaper`

| `imnoise`

| `otf2psf`

| `padarray`

| `psf2otf`

- Anonymous Functions (MATLAB)
- Parameterizing Functions (MATLAB)
- Create Function Handle (MATLAB)

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