# Parallel Dictionary Reconstruction and Fusion for Spectral Recovery in Computational Imaging Spectrometers

## TL;DR

This paper introduces a new method for improving spectral recovery in miniaturized imaging spectrometers using parallel dictionary reconstruction and fusion.

## Contribution

The novel approach combines parallel dictionary reconstruction with fusion to enhance spectral recovery accuracy and stability.

## Key findings

- The proposed method achieves a mean square recovery error of ≤1.73 × 10−4.
- It attains a recovery accuracy of ≥0.98 for visible-NIR spectral recovery of ground objects.
- The method is more universal and stable compared to traditional sparse representation methods.

## Abstract

Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10−4 and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods.