Image Deblurring with Coupled Dictionary Learning

摘要

Image deblurring is a challenging problem in vision computing. Traditionally, this task is addressed as an inverse problem that is enclosed into the image itself. This paper presents a learning-based framework where the knowledge hidden in huge amounts of available data is explored and exploited for image deblurring. To this end, our algorithm is developed under the conceptual framework of coupled dictionary learning. Specifically, given pairs of blurred image patches and their corresponding clear ones, a learning model is constructed to learn a pair of dictionaries. Among them, one dictionary is responsible for the representation of clear images, while the other is responsible for that of the blurred images. Theoretically, the learning model is analyzed with coupled sparse representations for training samples. As the atoms of these dictionaries are coupled together one-by-one, the reconstruction information can be transmitted between the clear and blurry images. In application phase, the blurry dictionary is employed to reconstruct linearly the blurry image to be restored. Then, the reconstruction coefficients are kept unchanged along with the clear dictionary to restore the final results. The main advantage of our approach lies in that it works in the case of unknown blur kernels. Comparative experiments indicate the validity of our approach.