A Review of FFT as a Sample Interpolator

Abstract

The Fast Fourier Transform (FFT) is a widely used algorithm in signal processing for efficiently converting signals between the time and frequency domains. This project explores the application of FFT as a sample interpolator, a novel approach to reconstructing missing or under-sampled data points in signals. Unlike traditional interpolation methods such as linear or polynomial techniques, FFT-based interpolation leverages the frequency-domain representation of signals to achieve higher accuracy, particularly for periodic and quasi-periodic data.

The project focuses on developing and implementing an FFT-based interpolation method, analyzing its computational efficiency and accuracy compared to standard techniques. By addressing challenges such as noise handling, computational complexity, and edge cases, this study aims to demonstrate the effectiveness of FFT for signal reconstruction. Practical applications of this approach include enhancing audio signals, refining image resolution, and improving data integrity in time-series analysis. The results are expected to showcase the potential of FFT as a robust and versatile tool for interpolation, offering valuable insights for both academic research and real-world applications.