13 Image Deblurring

LEN = 31;

THETA = 11;

PSF = fspecial('motion',LEN,THETA); % create PSF Blurred = imfilter(I,PSF,'circular','conv'); figure; imshow(Blurred); title('Blurred Image');

Deblurring Functions

The toolbox includes four deblurring functions, listed here in order of complexity. All the functions accept a PSF and the blurred image as their primary arguments.

deconvwnr Implements a least squares solution. You should provide some information about the noise to reduce possible noise amplification during deblurring. See “Deblurring with the Wiener Filter” on page 13-6 for more information.

deconvreg Implements a constrained least squares solution, where you can place constraints on the output image (the smoothness requirement is the default). You should provide some information about the noise to reduce possible noise amplification during deblurring. See “Deblurring with a Regularized Filter” on page 13-7 for more information.

13-4

Understanding Deblurring

deconvlucy Implements an accelerated, damped Lucy-Richardson algorithm. This function performs multiple iterations, using optimization techniques and Poisson statistics. You do not need to provide information about the additive noise in the corrupted image. See “Deblurring with the Lucy-Richardson Algorithm” on page 13-10 for more information.

deconvblind Implements the blind deconvolution algorithm, which performs deblurring without knowledge of the PSF. 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. See “Deblurring with the Blind Deconvolution Algorithm” on page 13-16 for more information.

When using the deblurring functions, note the following:

•Deblurring is an iterative process. You might need to repeat the deblurring process multiple times, 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.

•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” on page 13-24 for more information.

•For information about creating your own deblurring functions, see “Creating Your Own Deblurring Functions” on page 13-23.

13-5