# Color-Coded Compressive Spectral Imager Based on Focus Transformer Network

## TL;DR

This paper introduces a low-cost imaging system and a deep learning algorithm to improve the reconstruction of hyperspectral images.

## Contribution

A color-coded compressive spectral imaging system and a novel deep learning network called F-MST for efficient HSI reconstruction.

## Key findings

- The proposed system reduces system complexity and improves HSI reconstruction performance.
- The F-MST network outperforms existing reconstruction algorithms in terms of image quality.
- Simulations and experiments validate the effectiveness of the proposed method.

## Abstract

Compressive spectral imaging (CSI) methods aim to reconstruct a three-dimensional hyperspectral image (HSI) from a single or a few two-dimensional compressive measurements. Conventional CSIs use separate optical elements to independently modulate the light field in the spatial and spectral domains, thus increasing the system complexity. In addition, real applications of CSIs require advanced reconstruction algorithms. This paper proposes a low-cost color-coded compressive snapshot spectral imaging method to reduce the system complexity and improve the HSI reconstruction performance. The combination of a color-coded aperture and an RGB detector is exploited to achieve higher degrees of freedom in the spatio-spectral modulations, which also renders a low-cost miniaturization scheme to implement the system. In addition, a deep learning method named Focus-based Mask-guided Spectral-wise Transformer (F-MST) network is developed to further improve the reconstruction efficiency and accuracy of HSIs. The simulations and real experiments demonstrate that the proposed F-MST algorithm achieves superior image quality over commonly used iterative reconstruction algorithms and deep learning algorithms.