Robust Subspace Learning for Motion Deblurring in Images

We developed a framework for motion deblurring that finds a low rank approximation of the sharp image patches from a collection of blurry image patches. The approach relies on the notion that each blurry patch has undergone a different type of blur compared to the other patches. As a result, the low rank approximation of the group of patches recovers a sharp image component without the misalignment artifacts associated with a rank one approximation.