An efficient wavelet-based algorithm for image superresolution

Superresolution produces high quality, high resolution images from a set of degraded, low resolution frames. We present a new and efficient wavelet-based algorithm for image superresolution. The algorithm is a combination of interpolation and restoration processes. Unlike previous work, our method exploits the interlaced sampling structure in the low resolution data. Numerical experiments and analysis demonstrate the effectiveness of our approach and illustrate why the computational complexity only doubles for 2-D superresolution versus 1-D case.

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