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When the captured image is blurring, one way of obtaining a clear image is via image deconvolution technique. In order to perform deconvolution successfully, usually we need to pay attention to the following issues:

  1. Image content, which determines what kind of apriori information you will use (Total Variation, Laplacian and so on) during the image reconstruction procedure.
  2. Image noise level, which determines the balance factor between apriori information and data consistence term.
  3. Point Spread Function (PSF), which is used to measure how blurring the image is.

Here my question is related to PSF estimation. The simplest way of finding the right PSF is to use different PSFs to perform deconvolution, and then select the best one visually. Then my question is how we can tell which PSF is the best visually.

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You've asked about scoring your results visually. Do you mean a machine interpretation or a human interpretation? With humans, you have to deal with subjectivity. Seems like a visit to the eye doctor with a lot of A / B comparisons. There is a good paper that discusses quantitative teniques: "Learning a Bliind Measure of Perceptual Image Quality" –  B Z Jan 31 '13 at 14:32
One way is to not look at visual quality, but to impose prior constraints using properties that you know the image has (for example $1/f$). This paper has a lot of details on priors they use, and the result looks really impressive: cse.cuhk.edu.hk/~leojia/projects/motion_deblurring/… –  thang Feb 2 '13 at 1:19
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