Color router-based long-wave infrared multispectral imaging.

Abstract

In the field of long-wave infrared multispectral imaging, traditional snapshot techniques often deploy broadband filters in front of the sensor to encode spectral information about the scene. However, this approach causes a significant loss of precious optical energy, especially for the limited radiation energy of the long-wave infrared region. To address this issue, we first propose an imaging strategy that replaces conventional filters with specially designed diffractive elements, which are optimized by a gradient descent algorithm. The diffractive elements enable effective steering of diverse wavelengths to their designated pixels, significantly minimizing the reflection losses throughout light transmission and thereby augmenting the system's optical energy efficiency. Secondly, we use the MST neural network to reconstruct the spectral information and realize the snapshot computational multispectral imaging. In the experiments, we concentrate the wavelength band within 8-12 μm, simulating and optimizing the design of the diffractive elements. We also discuss how this innovative design can adapt to the field change of image plane that may be encountered in the actual imaging system. Emulation experiments show that our proposed method ensures excellent spectral separation and high imaging quality under different field conditions. This study provides new ideas and practical guidance for the lightweight and efficient development of long-wave infrared multispectral imaging technology.