CODEWITHSUNDEEP
matlabimage-processinggaussiansmoothing
BadProgrammer
BadProgrammer

Reputation: 99

Smoothing a noisy image then sharpening

I have been trying to restore a noisy image on MATLAB. I started with an original grayscale image of mine and then I applied Gaussian noise. I then took the noisy image and applied a Gaussian smoothing filter. After applying the smoothing filter, I applied a Laplacian filter over the Gaussian Blurred image and got a black image with some "edges" showing. What I am confused about is what to do next. I tried using the imadd function on MATLAB and adding the Gaussian blurred image with output of the Laplacian filter, but my results are not as good as I thought they would be. The "restored" image is nowhere near as good as I thought it would be!

Am I doing this correctly?

Upvotes: 1

Views: 1891

Answers (1)

rayryeng
rayryeng

Reputation: 104493

@eigenchris basically nailed it right on the head, but I would like to elaborate some more on why we believe this is a bad idea. Blurring the image removes high frequency content (i.e. edges). If you try to apply a high-pass filter like the Laplacian to the low-pass result, you will probably not get anything at all.

Specifically, the high frequency components were removed when you Gaussian blurred the image, and so if you apply a high-pass filter to an image with high frequency components already removed, you will probably get an almost zero output.

The moral of this story is that you can't sharpen an already blurred image because it relies on high frequency information to facilitate the sharpening. You are essentially amplifying the high frequency content so that the edges stand out more, and hence it is a sharpened result.

One thing I could suggest is to perhaps look into deconvolution techniques, like the Wiener filter. The Wiener filter essentially tries to undo the effects performed by a filter done on an image.

One great example can be found on this MathWorks link: http://www.mathworks.com/help/images/examples/deblurring-images-using-a-wiener-filter.html

As such, blur the image to eliminate any noise, then reverse the blur with Wiener filtering so you can get an OK version of the original, then sharpen that reconstructed image.

Good luck!

Upvotes: 2

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