No-reference blur assessment in natural images using Fourier transform and spatial pyramids

In this paper we propose a no-reference image blur assessment model that performs partial blur detection in the frequency domain. Specifically, our method exploits the information derived from the power spectrum of the Fourier transform. The latter is computed for both the entire image and several patches of it, in order to estimate the distribution of low and high frequencies, and is appropriately encoded so as to preserve some information about the spatial arrangement of the frequency distribution in the image. Finally, a Support Vector Machine (SVM) classifier is applied to the above features, serving as the image blur quality evaluator. For a proper training and evaluation of the proposed method, we proceeded with creating and using a large image dataset consisting of more than 2400 digital photographs, which we make publicly available. The results show the efficiency of our method in assessing not only artificially-distorted images but also naturally-blurred ones.