Spectrum Reconstruction of Multispectral Light Field Imager Based on Adaptive Sparse Representation

Spectra reflects the essential features of objects and has important applications in many fields, such as remote sensing, biology, food testing, and so on. The multispectral light field imager (MSLFI) can simultaneously capture spatial-spectral datacube and be used for detecting and monitoring dynamic targets. However, the spectral aliasing and high-level noise of the system are the main factors that degrade its spectral image quality and limit its practical applications. In this article, we propose an adaptive sparse representation (ASR) method to reconstruct the target spectrum of MSLFI with high noise levels. A rough result is reconstructed with a redundant dictionary at first. By recognizing peak features, the algorithm adaptively selects a sub-dictionary to conduct a secondary optimization. Besides, a position-constrained calibration method is introduced to obtain a denoised and sparse spectral aliasing matrix. The simulation and experimental results showed that the proposed ASR method can flexibly handle spectra with various curvatures and effectively improve the reconstruction accuracy of different spectra.