2,498 research outputs found
Etalon Array Reconstructive Spectrometry.
Compact spectrometers are crucial in areas where size and weight may need to be minimized. These types of spectrometers often contain no moving parts, which makes for an instrument that can be highly durable. With the recent proliferation in low-cost and high-resolution cameras, camera-based spectrometry methods have the potential to make portable spectrometers small, ubiquitous, and cheap. Here, we demonstrate a novel method for compact spectrometry that uses an array of etalons to perform spectral encoding, and uses a reconstruction algorithm to recover the incident spectrum. This spectrometer has the unique capability for both high resolution and a large working bandwidth without sacrificing sensitivity, and we anticipate that its simplicity makes it an excellent candidate whenever a compact, robust, and flexible spectrometry solution is needed
Simultaneous Measurement of Complementary Observables with Compressive Sensing
The more information a measurement provides about a quantum system’s position statistics, the less information a subsequent measurement can provide about the system’s momentum statistics. This information trade-off is embodied in the entropic formulation of the uncertainty principle. Traditionally, uncertainly relations correspond to resolution limits; increasing a detector’s position sensitivity decreases its momentum sensitivity and vice versa. However, this is not required in general; for example, position information can instead be extracted at the cost of noise in momentum. Using random, partial projections in position followed by strong measurements in momentum, we efficiently determine the transverse-position and transverse-momentum distributions of an unknown optical field with a single set of measurements. The momentum distribution is directly imaged, while the position distribution is recovered using compressive sensing. At no point do we violate uncertainty relations; rather, we economize the use of information we obtain
A switchable light field camera architecture with Angle Sensitive Pixels and dictionary-based sparse coding
We propose a flexible light field camera architecture that is at the convergence of optics, sensor electronics, and applied mathematics. Through the co-design of a sensor that comprises tailored, Angle Sensitive Pixels and advanced reconstruction algorithms, we show that-contrary to light field cameras today-our system can use the same measurements captured in a single sensor image to recover either a high-resolution 2D image, a low-resolution 4D light field using fast, linear processing, or a high-resolution light field using sparsity-constrained optimization.National Science Foundation (U.S.) (NSF Grant IIS-1218411)National Science Foundation (U.S.) (NSF Grant IIS-1116452)MIT Media Lab ConsortiumNational Science Foundation (U.S.) (NSF Graduate Research Fellowship)Natural Sciences and Engineering Research Council of Canada (NSERC Postdoctoral Fellowship)Alfred P. Sloan Foundation (Research Fellowship)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
Advances on CMOS image sensors
This paper offers an introduction to the technological advances of image sensors designed using
complementary metal–oxide–semiconductor (CMOS) processes along the last decades. We review
some of those technological advances and examine potential disruptive growth directions for CMOS
image sensors and proposed ways to achieve them. Those advances include breakthroughs on
image quality such as resolution, capture speed, light sensitivity and color detection and advances on
the computational imaging. The current trend is to push the innovation efforts even further as the
market requires higher resolution, higher speed, lower power consumption and, mainly, lower cost
sensors. Although CMOS image sensors are currently used in several different applications from
consumer to defense to medical diagnosis, product differentiation is becoming both a requirement and
a difficult goal for any image sensor manufacturer. The unique properties of CMOS process allows the
integration of several signal processing techniques and are driving the impressive advancement of the
computational imaging. With this paper, we offer a very comprehensive review of methods,
techniques, designs and fabrication of CMOS image sensors that have impacted or might will impact
the images sensor applications and markets
mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics
Hyperspectral imaging acquires data in both the spatial and frequency domains
to offer abundant physical or biological information. However, conventional
hyperspectral imaging has intrinsic limitations of bulky instruments, slow data
acquisition rate, and spatiospectral tradeoff. Here we introduce hyperspectral
learning for snapshot hyperspectral imaging in which sampled hyperspectral data
in a small subarea are incorporated into a learning algorithm to recover the
hypercube. Hyperspectral learning exploits the idea that a photograph is more
than merely a picture and contains detailed spectral information. A small
sampling of hyperspectral data enables spectrally informed learning to recover
a hypercube from an RGB image. Hyperspectral learning is capable of recovering
full spectroscopic resolution in the hypercube, comparable to high spectral
resolutions of scientific spectrometers. Hyperspectral learning also enables
ultrafast dynamic imaging, leveraging ultraslow video recording in an
off-the-shelf smartphone, given that a video comprises a time series of
multiple RGB images. To demonstrate its versatility, an experimental model of
vascular development is used to extract hemodynamic parameters via statistical
and deep-learning approaches. Subsequently, the hemodynamics of peripheral
microcirculation is assessed at an ultrafast temporal resolution up to a
millisecond, using a conventional smartphone camera. This spectrally informed
learning method is analogous to compressed sensing; however, it further allows
for reliable hypercube recovery and key feature extractions with a transparent
learning algorithm. This learning-powered snapshot hyperspectral imaging method
yields high spectral and temporal resolutions and eliminates the spatiospectral
tradeoff, offering simple hardware requirements and potential applications of
various machine-learning techniques.Comment: This paper will appear in PNAS Nexu
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