Compressive Sensing for Spectroscopy and Polarimetry

We demonstrate through numerical simulations with real data the feasibility of using compressive sensing techniques for the acquisition of spectro-polarimetric data. This allows us to combine the measurement and the compression process into one consistent framework. Signals are recovered thanks to a sparse reconstruction scheme from projections of the signal of interest onto appropriately chosen vectors, typically noise-like vectors. The compressibility properties of spectral lines are analyzed in detail. The results shown in this paper demonstrate that, thanks to the compressibility properties of spectral lines, it is feasible to reconstruct the signals using only a small fraction of the information that is measured nowadays. We investigate in depth the quality of the reconstruction as a function of the amount of data measured and the influence of noise. This change of paradigm also allows us to define new instrumental strategies and to propose modifications to existing instruments in order to take advantage of compressive sensing techniques.Comment: 11 pages, 9 figures, accepted for publication in A&

Full-Color Stereoscopic Imaging With a Single-Pixel Photodetector

We present an optical system for stereoscopic color imaging by using a single-pixel detector. The system works by illuminating the input scene with a sequence of microstructured light patterns generated by a color digital light projector (DLP). A single monochromatic photodiode, synchronized with the DLP, measures the light scattered by the object for each pattern. The image is recovered computationally by applying compressive sensing techniques. The RGB chromatic components of the image are discriminated by exploiting the time-multiplexed color codification of the DLP. The stereoscopic pair is obtained by splitting the light field generated by the DLP and projecting microstructured light patterns onto the sample from two different directions. The experimental setup is configured by simple optical components, a commercial photodiode and an off-the-shelf DLP projector. Color stereoscopic images of a 3D scene obtained with this system are shown.This work was supported in part by MINECO under Grant FIS2013-40666-P, Generalitat Valenciana under Grant PROMETEO2012-021 and Grant ISIC 2012/013, and Universitat Jaume I under Grant P1-1B2012-55

Roadmap on optical security

Postprint (author's final draft

Spinning Metasurface Stack for Spectro-polarimetric Thermal Imaging

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffer from limitations in size, spectral resolution and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce a novel approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressed sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro polarimetric stack is compact (< 10 x 10 x 10 cm), robust, and offers a wide field of view (20.5{\deg}). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR)

High-resolution adaptive imaging with a single photodiode

During the past few years, the emergence of spatial light modulators operating at the tens of kHz has enabled new imaging modalities based on single-pixel photodetectors. The nature of single-pixel imaging enforces a reciprocal relationship between frame rate and image size. Compressive imaging methods allow images to be reconstructed from a number of projections that is only a fraction of the number of pixels. In microscopy, single-pixel imaging is capable of producing images with a moderate size of 128 × 128 pixels at frame rates under one Hz. Recently, there has been considerable interest in the development of advanced techniques for high-resolution real-time operation in applications such as biological microscopy. Here, we introduce an adaptive compressive technique based on wavelet trees within this framework. In our adaptive approach, the resolution of the projecting patterns remains deliberately small, which is crucial to avoid the demanding memory requirements of compressive sensing algorithms. At pattern projection rates of 22.7 kHz, our technique would enable to obtain 128 × 128 pixel images at frame rates around 3 Hz. In our experiments, we have demonstrated a cost-effective solution employing a commercial projection display