Recovery of degraded images due to motion blurring is one challenging problem in digital imaging. Most existing techniques on blind deblurring are not capable of removing complex motion blurring from the blurred images of complex structures. One promising approach is to recover the clear image using multiple images captured for the scene. However, it is observed in practice that such a multi-frame approach can recover a high-quality clear image of the scene only after multiple blurred image frames are accurately aligned during pre-processing, which is a very challenging task even with user interactions. In this paper, by exploring the sparsity of the motion blur kernel and the clear image under certain domains, we proposed an alternative iteration approach to simultaneously identify the blur kernels of given blurred images and restore a clear image. Our proposed approach not only is robust to image formation noises, but also is robust to the alignment errors among multiple images. A modified version of linearized Bregman iteration is then developed to efficiently solve the resulting minimization problem. The experiments showed that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with minimal requirements on the accuracy of image alignment. As a result, our method is capable of automatically recovering a high-quality clear image from multiple blurred images.
In REF the authors showed that given multiple observations, the sparsity of the image under a tight frame is a good measurement of the clearness of the recovered image.
Blind motion deblurring using multiple images
Computer Science Mathematics