Fast Non-uniform Deblurring using Constrained Camera Pose Subspace
Zhe Hu, Ming–Hsuan Yang · 2012
where Kθ is the matrix that warps latent image L to the transformed copy at a sampled pose θ and S denotes the set of sampled camera poses. While these algorithms show promising results, they entail high computational cost as the high-dimensional camera motion space and the latent image have to be computed during the iterative optimization procedures. In this paper, we propose a fast single-image deblurring algorithm to remove non-uniform blur. We first introduce an initialization method that facilitates convergence and avoid local minimums of the formulated optimization problem. We then propose a new camera motion estimation method which optimizes on a small set of pose weights of a constrained camera pose subspace at a time rather than using the entire space. We develop an iterative method to refine the camera motion estimation and introduce perturbation at each iteration to obtain robust solutions. Fig. 1 summarizes the main steps of our method.