FFT Multichannel Interpolation and Application to Image Super-resolution

TL;DR

This paper introduces a fast, FFT-based multichannel interpolation method capable of exactly reconstructing bandlimited signals from finite samples, with applications to efficient image super-resolution.

Contribution

It proposes a novel multichannel interpolation technique using FFT for high efficiency and applies it to improve image super-resolution accuracy and speed.

Findings

Exact reconstruction of bandlimited signals from finite samples

High computational efficiency demonstrated by FFT-based algorithm

Superior super-resolution performance compared to state-of-the-art methods

Abstract

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.