Image deblurring with filters learned by extreme learning machine

Image deblurring is a basic and important task of image processing. Traditional filtering based image deblurring methods, e.g. enhancement filters, partial differential equation (PDE) and etc., are limited by the hypothesis that natural images and noise are with low and high frequency terms, respectively. Noise removal and edge protection are always the dilemma for traditional models.In this paper, we study image deblurring problem from a brand new perspective—classification. And we also generalize the traditional PDE model to a more general case, using the theories of calculus of variations. Furthermore, inspired by the theories of approximation of functions, we transform the operator-learning problem into a coefficient-learning problem by means of selecting a group of basis, and build a filter-learning model. Based on extreme learning machine (ELM) [1], [2], [3] and [4], an algorithm is designed and a group of filters are learned effectively. Then a generalized image deblurring model, learned filtering PDE (LF-PDE), is built.The experiments verify the effectiveness of our models and the corresponding learned filters. It is shown that our model can overcome many drawbacks of the traditional models and achieve much better results.