Simultaneous Surface Reflectance and Fluorescence Spectra Estimation

TL;DR

This paper introduces a novel method for jointly estimating surface reflectance and fluorescence spectra from multiple images under different illuminants, using a linear approximation and ADMM optimization, achieving higher accuracy than prior methods.

Contribution

The paper presents a new joint estimation framework that simplifies the inverse problem for surface reflectance and fluorescence spectra using a linear model and ADMM, with a software implementation and experimental validation.

Findings

Lower estimation errors compared to previous algorithms.

Effective joint estimation from a single image set under multiple illuminants.

Validated with both simulated and real experimental data.

Abstract

There is widespread interest in estimating the fluorescence properties of natural materials in an image. However, the separation between reflected and fluoresced components is difficult, because it is impossible to distinguish reflected and fluoresced photons without controlling the illuminant spectrum. We show how to jointly estimate the reflectance and fluorescence from a single set of images acquired under multiple illuminants. We present a framework based on a linear approximation to the physical equations describing image formation in terms of surface spectral reflectance and fluorescence due to multiple fluorophores. We relax the non-convex, inverse estimation problem in order to jointly estimate the reflectance and fluorescence properties in a single optimization step and we use the Alternating Direction Method of Multipliers (ADMM) approach to efficiently find a solution. We provide a software implementation of the solver for our method and prior methods. We evaluate the accuracy and reliability of the method using both simulations and experimental data. To acquire data to test the methods, we built a custom imaging system using a monochrome camera, a filter wheel with bandpass transmissive filters and a small number of light emitting diodes. We compared the system and algorithm performance with the ground truth as well as with prior methods. Our approach produces lower errors compared to earlier algorithms.