Multispectral Imaging with Fresnel Lens

TL;DR

This paper introduces a cost-effective multispectral imaging method using a single diffractive lens and deep learning to reconstruct 50 spectral channels, enabling compact and high-resolution spectral imaging.

Contribution

The novel approach combines a diffractive optical element with deep learning for spectral reconstruction, avoiding high costs and resolution compromises of traditional MSI techniques.

Findings

Reconstructed up to 50 spectral channels using physical diffraction theory and deep learning.

Achieved high spatial resolution without sacrificing image quality.

Demonstrated feasibility of a compact MSI camera for mobile integration.

Abstract

This paper presents a Multispectral imaging (MSI) approach that combines the use of a diffractive optical element, and a deep learning algorithm for spectral reconstruction. Traditional MSI techniques often face challenges such as high costs, compromised spatial or spectral resolution, or prolonged acquisition times. In contrast, our methodology uses a single diffractive lens, a grayscale sensor, and an optical motor to capture the Multispectral image without sacrificing spatial resolution, however with some temporal domain redundancy. Through an experimental demonstration, we show how we can reconstruct up to 50 spectral channel images using diffraction physical theory and a UNet-based deep learning algorithm. This approach holds promise for a cost-effective, compact MSI camera that could be feasibly integrated into mobile devices.