Programmable Spectral Filter Arrays using Phase Spatial Light Modulator
This work enables practical, high-resolution spectral filtering for applications like hyperspectral imaging and material classification, representing an incremental improvement by addressing a specific bottleneck in existing SLM setups.
The authors tackled the problem of optical aberrations in phase spatial light modulators (SLMs) for spatially varying spectral modulation, achieving ideal spectral modulation at high spatial resolution through a computational approach that minimizes aberrations and uses a deep network to correct residuals.
Spatially varying spectral modulation can be implemented using a liquid crystal spatial light modulator (SLM) since it provides an array of liquid crystal cells, each of which can be purposed to act as a programmable spectral filter array. However, such an optical setup suffers from strong optical aberrations due to the unintended phase modulation, precluding spectral modulation at high spatial resolutions. In this work, we propose a novel computational approach for the practical implementation of phase SLMs for implementing spatially varying spectral filters. We provide a careful and systematic analysis of the aberrations arising out of phase SLMs for the purposes of spatially varying spectral modulation. The analysis naturally leads us to a set of "good patterns" that minimize the optical aberrations. We then train a deep network that overcomes any residual aberrations, thereby achieving ideal spectral modulation at high spatial resolution. We show a number of unique operating points with our prototype including dynamic spectral filtering, material classification, and single- and multi-image hyperspectral imaging.