|Image Processing Toolbox User's Guide|
Deblurring with a Regularized Filter
deconvreg function to deblur an image using a regularized filter. A regularized filter can be used effectively when limited information is known about the additive noise.
To illustrate, this example simulates a blurred image by convolving a Gaussian filter PSF with an image (using
imfilter). Additive noise in the image is simulated by adding Gaussian noise of variance
V to the blurred image (using
I = imread('tissue.png'); I = I(125+[1:256],1:256,:); figure; imshow(I); title('Original Image');
Blurred = imfilter(I,PSF,'conv'); V = .02; BlurredNoisy = imnoise(Blurred,'gaussian',0,V); figure;imshow(BlurredNoisy);title('Blurred and Noisy Image');
deconvregto deblur the image, specifying the PSF used to create the blur and the noise power,
NP = V*prod(size(I)); [reg1 LAGRA] = deconvreg(BlurredNoisy,PSF,NP); figure,imshow(reg1),title('Restored Image');
Refining the Result
You can affect the deconvolution results by providing values for the optional arguments supported by the
deconvreg function. Using these arguments you can specify the noise power value, the range over which
deconvreg should iterate as it converges on the optimal solution, and the regularization operator to constrain the deconvolution. To see the impact of these optional arguments, view the Image Processing Toolbox deblurring demos.
|Deblurring with the Wiener Filter||Deblurring with the Lucy-Richardson Algorithm|
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