Abstract
The purpose of this paper is two-fold. First, we propose two new blind image deconvolution (BID) methods by improving Ahmed's BID method [1] in 2014 that is based on techniques of low-rank matrix recovery. The first method is introducing the total variation regularization term into Ahmed's BID method for the single-input-single-output (SISO) imaging model. The second method is extending Ahmed's BID method to the single-input-multiple-output (SIMO) imaging model. The practical iterative algorithm is developed to solve the formulated BID problem in each case when we take so-called iterative singular value thresholding algorithm. In the next part, we apply the new algorithm for the SIMO case, which is more stable than the SISO case, to the problem in generating all-in-focus images. We often have such a kind of problem when we take multiple images with different focal lengths for a 3-D scene holding varying depth. We demonstrate performances of the proposed methods through simulation studies as well as real data experiments.
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