Robustness of automated data choices of smoothing parameter in image regularization |
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Authors: | Karen Chan Alison Gray |
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Institution: | (1) Department of Computing Science and Mathematics, University of Stirling, FK9 4LA Stirling, UK;(2) Department of Statistics and Modelling Science, University of Strathclyde, G1 1XH Glasgow, UK |
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Abstract: | Statistical image restoration techniques are oriented mainly toward modelling the image degradation process in order to recover the original image. This usually involves formulating a criterion function that will yield some optimal estimate of the desired image. Often these techniques assume that the point spread function is known when the image is restored and indeed when we estimate the smoothing parameter. However in practice this assumption may not hold. This paper investigates empirically the effect of mis-specifying the point spread function on some data-based estimates of the regularization parameter and hence on the image reconstructions. Comparisons of image reconstruction quality are based on the mean absolute difference in pixel intensities between the true and reconstructed images. |
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Keywords: | Blurred images cross-validation image regularization point spread function smoothing parameter estimation |
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