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Deblur image using Lucy-Richardson method

restores image `J`

= deconvlucy(`I`

,`psf`

)`I`

that was degraded by convolution with a
point-spread function (PSF), `psf`

, and possibly by additive
noise. The algorithm is based on maximizing the likelihood that the resulting image
`J`

is an instance of the original image
`I`

under Poisson statistics.

To improve the restoration, `deconvlucy`

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`

= deconvlucy(`I`

,`psf`

,`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.

You can use

`deconvlucy`

to perform a deconvolution that starts where a previous deconvolution stopped. To use this feature, pass the input image`I`

as a cell array,`{I}`

. When you do, the`deconvlucy`

function returns the output image`J`

as a cell array, which you can then pass as the input array into the next`deconvlucy`

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,psf)`

before calling`deconvlucy`

.`deconvlucy`

converts the PSF to`double`

without normalization.`deconvlucy`

may return values in the output image that are beyond the range of the input image.

[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.