Adaptive image restoration using a local neural approach
Ignazio Gallo, Elisabetta Binaghi and Aldo Macchi
VISAPP 2007: Proceedings of the Second International Conference on Computer Vision Theory and Applications Volume 1, pp. 161-164, 2007.

Abstract

This work aims at defining and experimentally evaluating an iterative strategy based on neural learning for blind image restoration in the presence of blur and noise. A salient aspect of our solution is the local estimation of the restored image based on gradient descent strategies able to estimate both the blurring function and the regularized terms adaptively. Instead of explicitly defining the values of local regularization parameters through predefined functions, an adaptive learning approach is proposed. The method was evaluated experimentally using a test pattern generated by a function checkerboard in Matlab. To investigate whether the strategy can be considered an alternative to conventional restoration procedures the results were compared with those obtained by a well known neural restoration approach.

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