13 Image Deblurring

Deblurring with the Blind Deconvolution Algorithm

Use the deconvblind function to deblur an image using the blind deconvolution algorithm. The algorithm maximizes the likelihood that the resulting image, when convolved with the resulting PSF, is an instance of the blurred image, assuming Poisson noise statistics. The blind deconvolution algorithm can be used effectively when no information about the distortion (blurring and noise) is known. The deconvblind function restores the image and the PSF simultaneously, using an iterative process similar to the accelerated, damped Lucy-Richardson algorithm.

The deconvblind function, just like the deconvlucy function, implements several adaptations to the original Lucy-Richardson maximum likelihood algorithm that address complex image restoration tasks. Using these adaptations, you can

•Reduce the effect of noise on the restoration

•Account for nonuniform image quality (e.g., bad pixels)

•Handle camera read-out noise

For more information about these adaptations, see “Deblurring with the Lucy-Richardson Algorithm” on page 13-10. In addition, the deconvblind function supports PSF constraints that can be passed in through a user-specified function.

Example: Using the deconvblind Function to Deblur an Image

To illustrate blind deconvolution, this example creates a simulated blurred image and then uses deconvblind to deblur it. The example makes two passes at deblurring the image to show the effect of certain optional parameters

on the deblurring operation:

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Deblurring with the Blind Deconvolution Algorithm

1Read an image into the MATLAB workspace.

I = imread('cameraman.tif'); figure, imshow(I) title('Original Image')

2Create the PSF.

PSF = fspecial('motion',13,45);

figure, imshow(PSF,[],'InitialMagnification','fit') title('Original PSF')

3Create a simulated blur in the image.

Blurred = imfilter(I,PSF,'circ','conv'); figure, imshow(Blurred)

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13 Image Deblurring

title('Blurred Image')

4Deblur the image, making an initial guess at the size of the PSF.

To determine the size of the PSF, examine the blurred image and measure the width of a blur (in pixels) around an obviously sharp object. In the sample blurred image, you can measure the blur near the contour of the man’s sleeve. Because the size of the PSF is more important than the values it contains, you can typically specify an array of 1’s as the initial PSF.

The following figure shows a restoration where the initial guess at the PSF is the same size as the PSF that caused the blur. In a real application, you might need to rerun deconvblind, experimenting with PSFs of different sizes, until you achieve a satisfactory result. The restored PSF returned by each deconvolution can also provide valuable hints at the optimal PSF size. See the Image Processing Toolbox deblurring example.

INITPSF = ones(size(PSF));

[J P]= deconvblind(Blurred,INITPSF,30); figure, imshow(J)

title('Restored Image')

figure, imshow(P,[],'InitialMagnification','fit') title('Restored PSF')

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Deblurring with the Blind Deconvolution Algorithm

Although deconvblind was able to deblur the image to a great extent, the ringing around the sharp intensity contrast areas in the restored image is unsatisfactory. (The example eliminated edge-related ringing by using

the 'circular' option with imfilter when creating the simulated blurred image in step 3.)

The next steps in the example repeat the deblurring process, attempting to achieve a better result by

•Eliminating high-contrast areas from the processing

•Specifying a better PSF

5Create a WEIGHT array to exclude areas of high contrast from the deblurring operation. This can reduce contrast-related ringing in the result.

To exclude a pixel from processing, you create an array of the same size as the original image, and assign the value 0 to the pixels in the array that correspond to pixels in the original image that you want to exclude from processing. (See “Accounting for Nonuniform Image Quality” on page 13-11 for information about WEIGHT arrays.)

To create a WEIGHT array, the example uses a combination of edge detection and morphological processing to detect high-contrast areas in the image. Because the blur in the image is linear, the example dilates the image twice. (For more information about using these functions, see“Morphological Filtering” .) To exclude the image boundary pixels (a high-contrast area)

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13 Image Deblurring

from processing, the example uses padarray to assign the value 0 to all border pixels.

WEIGHT = edge(I,'sobel',.28); se1 = strel('disk',1);

se2 = strel('line',13,45);

WEIGHT = ~imdilate(WEIGHT,[se1 se2]);

WEIGHT = padarray(WEIGHT(2:end-1,2:end-1),[2 2]); figure, imshow(WEIGHT)

title('Weight Array')

6Refine the guess at the PSF. The reconstructed PSF P returned by the first pass at deconvolution shows a clear linearity, as shown in the figure in step 4. For the second pass, the example uses a new PSF, P1, which is the same as the restored PSF but with the small amplitude pixels set to 0.

P1 = P;

P1(find(P1 < 0.01))=0;

7Rerun the deconvolution, specifying the WEIGHT array and the modified PSF. Note how the restored image has much less ringing around the sharp intensity contrast areas than the result of the first pass (step 4).

[J2 P2] = deconvblind(Blurred,P1,50,[],WEIGHT); figure, imshow(J2)

title('Newly Deblurred Image');

figure, imshow(P2,[],'InitialMagnification','fit') title('Newly Reconstructed PSF')

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