Image sharpness classification system

摘要

A method for predicting whether a test image (318) is sharp or blurred includes the steps of: providing a sharpness classifier (316) that is trained to discriminate between sharp and blurred images; computing a set of sharpness features (322) for the test image (318) by (i) generating a high pass image (404) from the test image (318), (ii) generating a band pass image (406) from the test image (318), (iii) identifying textured regions (408) in the high pass image, (iv) identifying texture regions (410) in the band pass image, and (v) evaluating the identified textured regions in the high pass image and the band pass image to compute the set of test sharpness features (412); and evaluating the sharpness features using the sharpness classifier (324) to estimate if the test image (318) is sharp or blurry (20).

主权项

1. A method for determining if a test image is either sharp or blurred, the method comprising the steps of;
training a sharpness classifier to discriminate between sharp and blurred images, the sharpness classifier is trained based on a set of training sharpness features computed from a plurality of training images; and applying the trained classifier to the test image to determine if the test image is sharp or blurred based on a set of test sharpness features computed from the test image; wherein, the set of training sharpness features for each training image and the set of test sharpness features for each test image are computed by (i) preprocessing the respective image; (ii) generating a high pass image from the preprocessed image; (iii) generating a band pass image from the preprocessed image; (iv) identifying textured regions in the high pass image; (v) identifying texture regions in the band pass image; and (vi) computing the set of sharpness features from the identified textured regions.