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Image Super-Resolution Based on Structure-Modulated Sparse Representation

Image Super-Resolution Based on Structure-Modulated Sparse Representation

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 Image Super-Resolution Based on Structure-Modulated Sparse Representation

 Sparse representation has recently attracted enormous interests in the ?eld of image restoration. The conventional spar sity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of spar sity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure modulated sparse representations to improve the performance of spar sity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for higher solution image recovery. The multistep magni?cation scheme with the ridge regression is ?rst used to exploit the multi scale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more ?ne structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.


 


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  • Model: PROJ5006
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Saturday 03 September, 2016.

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