In this paper, we develop a regularization framework for image deblurring based on a new definition of the
normalized graph Laplacian. We apply a fast scaling algorithm to the kernel similarity matrix to derive the
symmetric, doubly stochastic filtering matrix from which the normalized Laplacian matrix is built. We use this
new definition of the Laplacian to construct a cost function consisting of data fidelity and regularization terms
to solve the ill-posed motion deblurring problem. The final estimate is obtained by minimizing the resulting cost
function in an iterative manner. Furthermore, the spectral properties of the Laplacian matrix equip us with the
required tools for spectral analysis of the proposed method. We verify the effectiveness of our iterative algorithm
via synthetic and real examples.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.