710 research outputs found
Generalized Inpainting Method for Hyperspectral Image Acquisition
A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding,
sparse models, CMOS, Fabry-P\'ero
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Quantum-inspired computational imaging
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.Y.A. acknowledges support from the UK Royal Academy of Engineering under the Research Fellowship Scheme (RF201617/16/31). S.McL. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grant EP/J015180/1). V.G. acknowledges support from the U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office award W911NF-10-1-0404, the U.S. DARPA REVEAL program through contract HR0011-16-C-0030, and U.S. National Science Foundation through grants 1161413 and 1422034. A.H. acknowledges support from U.S. Army Research Office award W911NF-15-1-0479, U.S. Department of the Air Force grant FA8650-15-D-1845, and U.S. Department of Energy National Nuclear Security Administration grant DE-NA0002534. D.F. acknowledges financial support from the UK Engineering and Physical Sciences Research Council (grants EP/M006514/1 and EP/M01326X/1). (RF201617/16/31 - UK Royal Academy of Engineering; EP/J015180/1 - UK Engineering and Physical Sciences Research Council; EP/M006514/1 - UK Engineering and Physical Sciences Research Council; EP/M01326X/1 - UK Engineering and Physical Sciences Research Council; W911NF-10-1-0404 - U.S. Defense Advanced Research Projects Agency (DARPA) InPho program through U.S. Army Research Office; HR0011-16-C-0030 - U.S. DARPA REVEAL program; 1161413 - U.S. National Science Foundation; 1422034 - U.S. National Science Foundation; W911NF-15-1-0479 - U.S. Army Research Office; FA8650-15-D-1845 - U.S. Department of the Air Force; DE-NA0002534 - U.S. Department of Energy National Nuclear Security Administration)Accepted manuscrip
Compressive Sensing Based Bio-Inspired Shape Feature Detection CMOS Imager
A CMOS imager integrated circuit using compressive sensing and bio-inspired detection is presented which integrates novel functions and algorithms within a novel hardware architecture enabling efficient on-chip implementation
Comparative noise performance of a coded aperture spectral imager
Novel types of spectral sensors using coded apertures may offer various advantages over conventional designs, especially the possibility of compressive measurements that could exceed the expected spatial, temporal or spectral resolution of the system. However, the nature of the measurement process imposes certain limitations, especially on the noise performance of the sensor. This paper considers a particular type of coded-aperture spectral imager and uses analytical and numerical modelling to compare its expected noise performance with conventional hyperspectral sensors. It is shown that conventional sensors may have an advantage in conditions where signal levels are high, such as bright light or slow scanning, but that coded-aperture sensors may be advantageous in low-signal condition
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
We present a framework for the end-to-end optimization of metasurface imaging
systems that reconstruct targets using compressed sensing, a technique for
solving underdetermined imaging problems when the target object exhibits
sparsity (i.e. the object can be described by a small number of non-zero
values, but the positions of these values are unknown). We nest an iterative,
unapproximated compressed sensing reconstruction algorithm into our end-to-end
optimization pipeline, resulting in an interpretable, data-efficient method for
maximally leveraging metaoptics to exploit object sparsity. We apply our
framework to super-resolution imaging and high-resolution depth imaging with a
phase-change material: in both situations, our end-to-end framework
computationally discovers optimal metasurface structures for compressed sensing
recovery, automatically balancing a number of complicated design
considerations. The optimized metasurface imaging systems are robust to noise,
significantly improving over random scattering surfaces and approaching the
ideal compressed sensing performance of a Gaussian matrix, showing how a
physical metasurface system can demonstrably approach the mathematical limits
of compressed sensing
Computational Spectral Imaging: A Contemporary Overview
Spectral imaging collects and processes information along spatial and
spectral coordinates quantified in discrete voxels, which can be treated as a
3D spectral data cube. The spectral images (SIs) allow identifying objects,
crops, and materials in the scene through their spectral behavior. Since most
spectral optical systems can only employ 1D or maximum 2D sensors, it is
challenging to directly acquire the 3D information from available commercial
sensors. As an alternative, computational spectral imaging (CSI) has emerged as
a sensing tool where the 3D data can be obtained using 2D encoded projections.
Then, a computational recovery process must be employed to retrieve the SI. CSI
enables the development of snapshot optical systems that reduce acquisition
time and provide low computational storage costs compared to conventional
scanning systems. Recent advances in deep learning (DL) have allowed the design
of data-driven CSI to improve the SI reconstruction or, even more, perform
high-level tasks such as classification, unmixing, or anomaly detection
directly from 2D encoded projections. This work summarises the advances in CSI,
starting with SI and its relevance; continuing with the most relevant
compressive spectral optical systems. Then, CSI with DL will be introduced, and
the recent advances in combining the physical optical design with computational
DL algorithms to solve high-level tasks
Smart Sensor Networks For Sensor-Neural Interface
One in every fifty Americans suffers from paralysis, and approximately 23% of paralysis cases are caused by spinal cord injury. To help the spinal cord injured gain functionality of their paralyzed or lost body parts, a sensor-neural-actuator system is commonly used. The system includes: 1) sensor nodes, 2) a central control unit, 3) the neural-computer interface and 4) actuators. This thesis focuses on a sensor-neural interface and presents the research related to circuits for the sensor-neural interface.
In Chapter 2, three sensor designs are discussed, including a compressive sampling image sensor, an optical force sensor and a passive scattering force sensor. Chapter 3 discusses the design of the analog front-end circuit for the wireless sensor network system. A low-noise low-power analog front-end circuit in 0.5μm CMOS technology, a 12-bit 1MS/s successive approximation register (SAR) analog-to-digital converter (ADC) in 0.18μm CMOS process and a 6-bit asynchronous level-crossing ADC realized in 0.18μm CMOS process are presented. Chapter 4 shows the design of a low-power impulse-radio ultra-wide-band (IR-UWB) transceiver (TRx) that operates at a data rate of up to 10Mbps, with a power consumption of 4.9pJ/bit transmitted for the transmitter and 1.12nJ/bit received for the receiver. In Chapter 5, a wireless fully event-driven electrogoniometer is presented. The electrogoniometer is implemented using a pair of ultra-wide band (UWB) wireless smart sensor nodes interfacing with low power 3-axis accelerometers. The two smart sensor nodes are configured into a master node and a slave node, respectively. An experimental scenario data analysis shows higher than 90% reduction of the total data throughput using the proposed fully event-driven electrogoniometer to measure joint angle movements when compared with a synchronous Nyquist-rate sampling system.
The main contribution of this thesis includes: 1) the sensor designs that emphasize power efficiency and data throughput efficiency; 2) the fully event-driven wireless sensor network system design that minimizes data throughput and optimizes power consumption
Structured Compressed Sensing: From Theory to Applications
Compressed sensing (CS) is an emerging field that has attracted considerable
research interest over the past few years. Previous review articles in CS limit
their scope to standard discrete-to-discrete measurement architectures using
matrices of randomized nature and signal models based on standard sparsity. In
recent years, CS has worked its way into several new application areas. This,
in turn, necessitates a fresh look on many of the basics of CS. The random
matrix measurement operator must be replaced by more structured sensing
architectures that correspond to the characteristics of feasible acquisition
hardware. The standard sparsity prior has to be extended to include a much
richer class of signals and to encode broader data models, including
continuous-time signals. In our overview, the theme is exploiting signal and
measurement structure in compressive sensing. The prime focus is bridging
theory and practice; that is, to pinpoint the potential of structured CS
strategies to emerge from the math to the hardware. Our summary highlights new
directions as well as relations to more traditional CS, with the hope of
serving both as a review to practitioners wanting to join this emerging field,
and as a reference for researchers that attempts to put some of the existing
ideas in perspective of practical applications.Comment: To appear as an overview paper in IEEE Transactions on Signal
Processin
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