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Proceedings Paper

Model-based PDE method and model-free PDE method for motion de-blurring
Author(s): Takahiro Saito; Hiroyuki Harada; Takashi Komatsu
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Paper Abstract

Once image motion is accurately estimated, we can utilize those motion estimates for image sharpening and we can remove motion blurs. First, for the motion de-blurring, this paper presents a model-based PDE method that minimizes the regularized energy functional defined with a spatially variant model of motion blurs. Unlike the case of spatially invariant image blurs, the minimization of the energy functional cannot be achieved in a closed non-iterative way, and we derive its iterative algorithm. The standard regularization method uses a square function to measure energy of its solution function, and employs the energy functional composed of the data-fidelity energy term to measure a deviation of a solution function from the assumed model of motion blurs and the regularization energy term to impose smoothness constraints on a solution function. However, the standard variational method is not proper for the motion de-blurring, because it is sensitive to model errors, and occurrence of errors are inevitable in motion estimation. To improve the robustness against the model errors, we employ a nonlinear robust estimation function for measuring energy to be minimized. Secondly, this paper experimentally compares the model-based PDE method with our previously presented model-free PDE method that does not need any accurate blur model. In the model-error-free case the model-based PDE method outperforms the model-free PDE method, whereas in the model-error case the latter works better than the former.

Paper Details

Date Published: 24 June 2005
PDF: 12 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59605A (24 June 2005); doi: 10.1117/12.633209
Show Author Affiliations
Takahiro Saito, Kanagawa Univ. (Japan)
Hiroyuki Harada, Kanagawa Univ. (Japan)
Takashi Komatsu, Kanagawa Univ. (Japan)


Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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