Computational spectrometer based on local feature-weighted spectral reconstruction

The computational spectrometer enables the reconstruction of spectra from precalibrated information encoded. In the last decade, it has emerged as an integrated and low-cost paradigm with vast potential for applications, especially in portable or handheld spectral analysis devices. The conventional methods utilize a local-weighted strategy in feature spaces. These methods overlook the fact that the coefficients of important features could be too large to reflect differences in more detailed feature spaces during calculations. In this work, we report a local feature-weighted spectral reconstruction (LFWSR) method, and construct a high-accuracy computational spectrometer. Different from existing methods, the reported method learns a spectral dictionary via L4-norm maximization for representing spectral curve features, and considers the statistical ranking of features. According to the ranking, weight features and update coefficients then calculate the similarity. What's more, the inverse distance weighted is utilized to pick samples and weight a local training set. Finally, the final spectrum is reconstructed utilizing the local training set and measurements. Experiments indicate that the reported method's two weighting processes produce state-of-the-art high accuracy.

Automated summary

The computational spectrometer can reconstruct spectra based on precalibrated information. It has become an integrated and low-cost paradigm with great potential in portable or handheld spectral analysis devices in the past decade. Different from existing methods, the reported method learns a spectral dictionary via L4-norm maximization, considers statistical ranking of features, and utilizes inverse distance weighting for picking samples and constructing a local training set to achieve high accuracy.