2 research outputs found
Illuminant Spectra-based Source Separation Using Flash Photography
Real-world lighting often consists of multiple illuminants with different
spectra. Separating and manipulating these illuminants in post-process is a
challenging problem that requires either significant manual input or calibrated
scene geometry and lighting. In this work, we leverage a flash/no-flash image
pair to analyze and edit scene illuminants based on their spectral differences.
We derive a novel physics-based relationship between color variations in the
observed flash/no-flash intensities and the spectra and surface shading
corresponding to individual scene illuminants. Our technique uses this
constraint to automatically separate an image into constituent images lit by
each illuminant. This separation can be used to support applications like white
balancing, lighting editing, and RGB photometric stereo, where we demonstrate
results that outperform state-of-the-art techniques on a wide range of images
KRISM --- Krylov Subspace-based Optical Computing of Hyperspectral Images
We present an adaptive imaging technique that optically computes a low-rank
approximation of a scene's hyperspectral image, conceptualized as a matrix.
Central to the proposed technique is the optical implementation of two
measurement operators: a spectrally-coded imager and a spatially-coded
spectrometer. By iterating between the two operators, we show that the top
singular vectors and singular values of a hyperspectral image can be adaptively
and optically computed with only a few iterations. We present an optical design
that uses pupil plane coding for implementing the two operations and show
several compelling results using a lab prototype to demonstrate the
effectiveness of the proposed hyperspectral imager.Comment: 14 pages of main paper and 15 pages of supplementary materia