How to Select Point Spread Function Empirically for Image Deconvolution? 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:


Image content, which determines what kind of apriori information you will use (Total Variation, Laplacian and so on) during the image reconstruction procedure. 
Image noise level, which determines the balance factor between apriori information and data consistence term. 
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.
One way, though not directly visual, is to observe the image statistics.
We have a pretty good idea about the statistics of Natural Images, more specifically, their Gradient Distribution (See Statistics of Natural Images and Models by Jinggang Huang, D. Mumford, What Makes a Good Model of Natural Images by Yair Weiss, William T. Freeman, The Statistic Distribution of Image Gradient - Mathematics StackExchnage and UCLA Stat232A-CS266A Statistical Modeling and Learning in Vision and Cognition - Chapter 2 - Empirical Observations: Image Space and Natural Image Statistics).

So what you can do is see how well the statistics of the result matches the models.
Actually many modern (Prior to the Deep Learning era) Deblurring Methods work according to this approach with very nice results.

Look at Blind Motion Deblurring Using Image Statistics by Anat Levin.