Detecting and classifying blurred image regions

Many image deblurring algorithms perform blur kernel estimation and image deblurring by assuming the blur type and distribution are already known. However, in practice such information is not known in advance and must be estimated using local blur measures. In this paper, we revisit the image partial blur detection and classification problem and propose several new or enhanced local blur measures using different types of image information including color, gradient and spectral information. The proposed measures demonstrate stronger discriminative power, better across-image stability or higher computational efficiency than previous ones. By learning the optimal combination of these measures with SVM classifiers, we obtain a patch-based image partial blur detector and classifier. Experiments on a large dataset of real images show the proposed approach has superior performance to the state-of-the-art approach.