Zhang, Y and Kingsbury, N (2010) Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization. Proceedings - International Conference on Image Processing, ICIP. pp. 1685-1688. ISSN 1522-4880Full text not available from this repository.
Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.
|Uncontrolled Keywords:||Deconvolution Image restoration L0 norms Regularization Sparsity|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Unnamed user with email email@example.com|
|Date Deposited:||16 Jul 2015 14:11|
|Last Modified:||05 Sep 2015 00:58|