Step Size For The General Iterative Image Recovery Algorithm

1988 ◽  
Vol 27 (9) ◽  
Author(s):  
C. I. Podilchuk ◽  
R. J. Mammone
Author(s):  
John Y.Q. Zhang ◽  
Ben L. Yeung ◽  
Joe C.Y. Wong ◽  
Ray C.C. Cheung ◽  
Alan H.F. Lam

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Xinghui Zhu ◽  
Fang Kui

We proposed a recovery scheme for image deblurring. The scheme is under the framework of sparse representation and it has three main contributions. Firstly, considering the sparse property of natural image, the nonlocal overcompleted dictionaries are learned for image patches in our scheme. And, then, we coded the patches in each nonlocal clustering with the corresponding learned dictionary to recover the whole latent image. In addition, for some practical applications, we also proposed a method to evaluate the blur kernel to make the algorithm usable in blind image recovery. The experimental results demonstrated that the proposed scheme is competitive with some current state-of-the-art methods.


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