We present an efficient Newton-like algorithm for quasi-maximum likelihood (QML) blind deconvolution of images. This algorithm exploits the sparse structure of the Hessian. An optimal distribution-shaping approach by means of sparsification allows one to use simple and convenient sparsity prior for processing of a wide range of natural images. Simulation results demonstrate the efficiency of the proposed method.
|Number of pages||4|
|Journal||Proceedings - International Conference on Image Processing, ICIP|
|State||Published - 2004|
|Event||2004 International Conference on Image Processing, ICIP 2004 - , Singapore|
Duration: 18 Oct 2004 → 21 Oct 2004