Image Processing Toolbox User's Guide |

**Using the Deblurring Functions**

The toolbox includes four deblurring functions, listed here in order of complexity:

`deconvwnr` |
Implements deblurring using the Wiener filter |

`deconvreg` |
Implements deblurring using a regularized filter |

`deconvlucy` |
Implements deblurring using the Lucy-Richardson algorithm |

`deconvblind` |
Implements deblurring using the blind deconvolution algorithm |

All the functions accept a PSF and the blurred image as their primary arguments. The `deconvwnr`

function implements a least squares solution. The `deconvreg`

function implements a constrained least squares solution, where you can place constraints on the output image (the smoothness requirement is the default). With either of these functions, you should provide some information about the noise to reduce possible noise amplification during deblurring.

The `deconvlucy`

function implements an accelerated, damped Lucy-Richardson algorithm. This function performs multiple iterations, using optimization techniques and Poisson statistics. With this function, you do not need to provide information about the additive noise in the corrupted image.

The `deconvblind`

function implements the blind deconvolution algorithm, which performs deblurring without knowledge of the PSF. When you call `deconvblind`

, you pass as an argument your initial guess at the PSF. The `deconvblind`

function returns a restored PSF in addition to the restored image. The implementation uses the same damping and iterative model as the `deconvlucy`

function.

For information about creating your own deblurring functions, see Creating Your Own Deblurring Functions. To avoid "ringing" in a deblurred image, you can use the `edgetaper`

function to preprocess your image before passing it to the deblurring functions. See Avoiding Ringing in Deblurred Images for more information.

Deblurring Model | Deblurring with the Wiener Filter |

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