Programmable Spectral Filter Arrays using Phase Spatial Light Modulator

TL;DR

This paper introduces a computational method to enable high-resolution, spatially varying spectral filtering using phase SLMs by analyzing aberrations, identifying optimal patterns, and training a deep network to correct residual distortions.

Contribution

It presents a novel approach combining systematic aberration analysis, pattern optimization, and deep learning to implement practical, high-resolution spectral filters with phase SLMs.

Findings

Achieved high-resolution spectral modulation with minimized aberrations.

Demonstrated dynamic spectral filtering and hyperspectral imaging capabilities.

Validated the approach with a functional prototype showing multiple applications.

Abstract

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.