Robust hyperspectral reconstruction via a multi-channel clustering compressive sensing approach

Abstract

Wavelength-coded spectral imaging represents a fusion of spectral imaging and compressed sensing, offering advantages such as reduced storage requirements and straightforward miniaturization. This approach employs optical filters for hyperspectral reconstruction. However, issues like insufficient energy and excessive noise arise in low-light detection, leading to a notable degradation of image reconstruction quality. This paper presents a robust segmented reconstruction algorithm named MCC-WSCI (Multi-Channel Clustering-based Wavelength-Coded Spectral Imaging), designed to enhance the method's tolerance to noise. This algorithm can accurately classify and process compressed images obtained by wavelength-coded spectral imaging systems, thus improving reconstruction quality significantly. Numerical simulations and experiments in low-light scenarios are carried out to verify the proposed method. Results show that the MCC-WSCI method is robust to different noise and sampling rates and outperforms other state-of-the-art compressed sensing reconstruction methods in terms of reconstructed spatial resolution and spectral resolution. The proposed method provides effective experimental robustness to wavelength-coded spectral imaging with a natural algorithmic extension, paving the way for its application in remote sensing.