Accurate and robust image superresolution by neural processing of local image representations

Discussion

A superresolution method based on neural processing of local image representa-
tions has been developed. The method process representation coefficients on local
windows to estimate the HR image values. The dimensionality of input data to this
network is firstly reduced by application of a PCA technique. The eigenimages ob-
tained have been shown to be stable for a wide range of noise levels and image con-
tents, representing intrinsic properties of image structure at sub-pixel levels. Varia-
tions of prediction error with input size have been examined, showing a non-gradual,
structured behavior, reflecting the inadequacy of MSE heuristics in this problem. The
relation of the results with those provided by non-linear methods, such as ICA [12],
will be investigated in future work. The experimental results obtained show the accu-
racy and robustness of the developed method.

Acknowledgments

This work has been partially supported by grants BFI2003-07276 and TIN2004-
04363-C03-03, and by PROFIT project FIT-330100-2004-91.

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