{
  "id": "1408.4712",
  "version": "v1",
  "published": "2014-08-20T16:18:13.000Z",
  "updated": "2014-08-20T16:18:13.000Z",
  "title": "Bi-l0-l2-Norm Regularization for Blind Motion Deblurring",
  "authors": [
    "Wen-Ze Shao",
    "Hai-Bo Li",
    "Michael Elad"
  ],
  "comment": "13 pages, 18 figures",
  "categories": [
    "cs.CV"
  ],
  "abstract": "In blind motion deblurring, a commonly practiced approach is to perform the restoration in two stages: first, the blur-kernel is estimated along with a temporal restored image, and then a regular image deblurring algorithm is applied with the found kernel. This work addresses the first stage, for which a great deal of effort has been placed on the choice of the image and the blur-kernel priors. Leading methods today tend towards highly non-convex approximations of the l0-norm, especially in the image regularization term. In this paper, an effective approach is proposed for the estimation of the motion blur-kernel, through a bi-l0-l2-norm regularization, imposed on both the image and the blur-kernel. Compared with existing methods, the proposed regularization is shown to be more effective and robust in improving the accuracy of the estimated motion blur-kernel, thereby leading to a better final restored image. Our approach has the capability of genera- ting a higher quality intermediate sharp image with more accu- rate salient edges and less staircase and ringing artifacts, as well as an ability of reducing the possible isolated points and weak components in the estimated blur-kernel. We derive a fast nume- rical algorithm for computing alternatingly the sharp image and the motion blur-kernel, by coupling the operator splitting and the augmented Lagrangian methods, as well as using the fast Fourier transform (FFT). We also use a continuation strategy that dimi- nishes the strength of the regularization over the iterations, which we demonstrate to be beneficial to the motion-blur kernel estima- tion accuracy. Extensive experiments on a benchmark dataset and real-world motion blurred images demonstrate that the proposed approach is highly competitive with state-of-the-art methods in both deblurring effectiveness and computational efficiency.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-08-20T16:18:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "blind motion deblurring",
      "bi-l0-l2-norm regularization",
      "motion blur-kernel",
      "higher quality intermediate sharp image",
      "real-world motion blurred images demonstrate"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 13,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1408.4712S"
    }
  }
}