2. A computer-implemented method, the method comprising: applying a plurality of deblur transformations to a blurry image to produce a plurality of deblurred images; computing scores for the plurality of deblurred images according to qualities of deblurrings of the deblurred images; extracting, from image content of each of the plurality of deblurred images, an image feature vector characterizing one or more visual aspects of the image; training a classifier based on the plurality of image feature vectors and on the scores, the classifier taking as input one or more deblurred images and producing as output for each deblurred image a score quantifying deblurring quality of the deblurred image; and storing the classifier.
2. A computer-implemented method, the method comprising: applying a plurality of deblur transformations to a blurry image to produce a plurality of deblurred images; computing scores for the plurality of deblurred images according to qualities of deblurrings of the deblurred images; extracting, from image content of each of the plurality of deblurred images, an image feature vector characterizing one or more visual aspects of the image; training a classifier based on the plurality of image feature vectors and on the scores, the classifier taking as input one or more deblurred images and producing as output for each deblurred image a score quantifying deblurring quality of the deblurred image; and storing the classifier. 3. The method of claim 2 , further comprising: computing a frequency domain transform of each of the plurality of deblurred images to produce a plurality of frequency domain images; wherein the image feature vector describes a feature of the frequency domain images.