Compressive hyperspectral imaging using total variation minimization

Dennis J. Lee; Eric A. Shields

doi:10.1117/12.2322145

18 September 2018 Compressive hyperspectral imaging using total variation minimization

Dennis J. Lee, Eric A. Shields

Proceedings Volume 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing; 1076804 (2018) https://doi.org/10.1117/12.2322145
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

Compressive sensing shows promise for sensors that collect fewer samples than required by traditional Shannon-Nyquist sampling theory. Recent sensor designs for hyperspectral imaging encode light using spectral modulators such as spatial light modulators, liquid crystal phase retarders, and Fabry-Perot resonators. The hyperspectral imager consists of a filter array followed by a detector array. It encodes spectra with less measurements than the number of bands in the signal, making reconstruction an underdetermined problem. We propose a reconstruction algorithm for hyperspectral images encoded through spectral modulators. Our approach constrains pixels to be similar to their neighbors in space and wavelength, as natural images tend to vary smoothly, and it increases robustness to noise. It combines L₁ minimization in the wavelet domain to enforce sparsity and total variation in the image domain for smoothness. The alternating direction method of multipliers (ADMM) simplifies the optimization procedure. Our algorithm constrains encoded, compressed hyperspectral images to be smooth in their reconstruction, and we present simulation results to illustrate our technique. This work improves the reconstruction of hyperspectral images from encoded, multiplexed, and sparse measurements.

Conference Presentation

Citation Download Citation

Dennis J. Lee and Eric A. Shields "Compressive hyperspectral imaging using total variation minimization", Proc. SPIE 10768, Imaging Spectrometry XXII: Applications, Sensors, and Processing, 1076804 (18 September 2018); https://doi.org/10.1117/12.2322145

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