Using the Deblurring Functions :: Image Deblurring (Image Processing Toolbox User's Guide)

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

`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.

Note You might need to perform many iterations of the deblurring process, varying the parameters you specify to the deblurring functions with each iteration, until you achieve an image that, based on the limits of your information, is the best approximation of the original scene. Along the way, you must make numerous judgments about whether newly uncovered features in the image are features of the original scene or simply artifacts of the deblurring process.

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