Image deblurring with matrix regression and gradient evolution

This paper presents a supervised learning algorithm for image deblurring. The task is addressed into the conceptual framework of matrix regression and gradient evolution. Specifically, given pairs of blurred image patches and their corresponding clear ones, an optimization framework of matrix regression is proposed to learn a matrix mapping. For an image to be deblurred, the learned matrix mapping will be employed to map each of its image patches directly to be a new one with more sharp details. The mapped result is then analyzed in terms of edge profiles, and the image is finally deblurred in way of gradient evolution. The algorithm is fast, and easy to be implemented. Comparative experiments on diverse natural images and the applications to interactive deblurring of real-world out-of-focus images illustrate the validity of our method.

► Develop a supervised learning algorithm for image deblurring.
► Learn matrix mapping to replace conventional kernel estimation.
► Improve image quality with gradient evolution.
► Demonstrate its performance in image quality and computation time.
► Show its applications to interactive image deblurring.