  This paper presents an innovative set of tools to support a methodology for
the multichannel interpolation (MCI) of a discrete signal. It is shown that a
bandlimited signal $f$ can be exactly reconstructed from finite samples of
$g_k$ ($1\leq k\leq M$) which are the responses of $M$ linear systems with
input $f$. The proposed interpolation can also be applied to approximate
non-bandlimited signals. Quantitative error is analyzed to ensure its
effectiveness in approximating non-bandlimited signals and its Hilbert
transform. Based on the FFT technique, a fast algorithm which brings high
computational efficiency and reliability for MCI is presented. The standout
performance of MCI is illustrated by several simulations. Additionally, the
proposed interpolation is applied to the single image super-resolution (SISR).
Its superior performance in accuracy and speed of SISR is demonstrated by the
experimental studies. Our results are compared qualitatively and quantitatively
with the state-of-the-art methods in image upsampling and reconstruction by
using the standard measurement criteria.
