Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
849374 | Optik - International Journal for Light and Electron Optics | 2014 | 4 Pages |
Abstract
This work devotes to the image deconvolution problem that restores clear image from its blurred and noisy measurements with little prior about the blur. A deconvolution method based on sparse and redundant representation theory is developed in this paper. It firstly represents the blur and image over different redundant dictionaries and imposes sparsity constraint to their representation coefficients respectively, then alternately estimates them using an iterative algorithm employing optimization technique. Experimental results on astronomical images show that the proposed method can achieve as good performance as the method requiring a known blur, which demonstrates its effectiveness.
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Authors
Long Ma, Rongzhi Zhang, Zhiguo Qu, Fangyun Lu, Rong Xu,