资 源 简 介
Methods to undo the effects of motion blur are the subject of intense
research, but evaluating and tuning these algorithms has traditionally required either user input or the availability of ground-truth
images. We instead develop a metric for automatically predicting
the perceptual quality of images produced by state-of-the-art deblurring algorithms. The metric is learned based on a massive user
study, incorporates features that capture common deblurring artifacts, and does not require access to the original images (i.e., is “noreference”). We show that it better matches user-supplied rankings
than previous approaches to measuring quality, and that in most
cases it out
