IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY LETTERS 1 Blur-Kernel Bound Estimation from Pyramid Statistics

Abstract—This paper presents an approach for automatically estimating the spatial bound of the blur kernel in a motion-blurred image, based on the statistics of multi-level image gradients. We observe that blur has a significant impact on the Histogram of Oriented Gradients (HOG) at higher levels of an image pyramid, but has much less impact at coarser levels. Based on this fact we estimate the spatial bound of the unknown blur kernel using a learning-based approach. We first learn a generic pyramid HOG model from natural sharp images, then given a HOG pyramid of a blurry image, we predict the corresponding model of its latent sharp image. Finally, we learn another model to predict the spatial kernel bound from the difference between the observed and the predicted HOG pyramids. Experimental results show that the proposed method can estimate accurate blur kernel sizes, enabling existing blind deconvolution methods to achieve best possible results