Sparse representation based iterative incremental image deblurring

Authors:
Haichao Zhang;Yanning Zhang
Affiliations:
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Inspired by the observation that in image restoration, parametric models are extremely specific while pixel-level models are too loose, tending to under or over fit the underlying image respectively, in this paper, we proposed an 'intermediate-language' based method for image deblurring. The solution space is represented at a level higher than the pixel-grid level, while retain an enough degree of freedom (DOF), thus avoids the common local under or over fitting problem. Considering the sparseness property of images, a sparse representation based incremental iterative method is established for blurry image restoration. Comprehensive experiments demonstrate that the framework integrating the sparseness property of images significantly improves the deblurring performance.