|Image Processing Toolbox|
Deblurring with the Wiener Filter
deconvwnr function to deblur an image using the Wiener filter. Wiener deconvolution can be used effectively when the frequency characteristics of the image and additive noise are known, to at least some degree. In the absence of noise, the Wiener filter reduces to the ideal inverse filter.
This example deblurs the blurred flower image, created in Deblurring Model, specifying the same PSF function that was used to create the blur.
I = imread('flowers.tif'); I = I(10+[1:256],222+[1:256],:); % crop the image figure;imshow(I);title('Original Image'); % create PSF LEN = 31; THETA = 11; PSF = fspecial('motion',LEN,THETA); % blur the image Blurred = imfilter(I,PSF,'circular','conv'); figure; imshow(Blurred);title('Blurred Image'); % deblur the image wnr1 = deconvwnr(Blurred,PSF); figure;imshow(wnr1); title('Restored, True PSF');
This example illustrates the importance of knowing the PSF, the function that caused the blur. When you know the exact PSF, as in this example, the results of deblurring can be quite effective.
Refining the Result
You can affect the deconvolution results by providing values for the optional arguments supported by the
deconvwnr function. Using these arguments you can specify the noise-to-signal power value and/or provide autocorrelation functions to help refine the result of deblurring. To see the impact of these optional arguments, view the Image Processing Toolbox Deblurring Demos.
|Using the Deblurring Functions||Deblurring with a Regularized Filter|