Phase-coded aperture for optical imaging

Chi, Wanli; George, Nicholas

doi:10.1016/j.optcom.2009.02.031

Cited by 27 publications

(16 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CI techniques can be employed to synthesize quasi-random bases and relax the raster scanning requirements of SAR and phased array modalities. One such technique is the codedaperture technique, which uses a set of masks with spatiotemporarily varying complex weights [55]- [58], through which the transmitted wavefronts probing the imaged scene and the received wavefronts collecting the back-scattered information are passed. This spatio-temporal mapping aperture concept can be thought of as a large aperture that can radiate complex, quasi-random radiation patterns.…”

Section: Coded-aperture CI Systemmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

View full text Add to dashboard Cite

A millimeter-wave (mmW) classifier system applied to images synthesized from a codedaperture based computational imaging (CI) radar is presented. A developed physical model of a CI system is used to generate the image dataset for the classification algorithm. A convolutional neural network (CNN) is integrated with the physical model and trained using the dataset comprising of synthesized mmW images obtained directly from the developed CI physical model. A k-fold cross validation technique is applied during the training process to validate the classification model. The coded-aperture CI concept enables image reconstruction from a significantly reduced number of back-scattered measurements by facilitating physical layer compression. This physical layer compression can substantially simplify the data acquisition layer of imaging radars, which is realized using only two channels in this article. The integration of the classification algorithm with the CI numerical model is particularly important in enabling the training step to be carried out using relevant system metrics and without the necessity for experimental data. Leveraging the CI numerical model generated data, training step for the classification algorithm is achieved in real-time while also confirming that the numerically trained CI classifier offers high accuracy with both simulated and experimental data. The classifier integrated physical model also enables performance analysis of the classification algorithm to be carried out as a function of key system metrics such as signal-to-noise (SNR) level, ensuring a complete understanding of the classification accuracy under different operating conditions. The trained CI system is tested with synthesized mmW images from the physical model and a classification accuracy of 89% is achieved. The proposed model is also verified using experimental data validating the fidelity of the developed CI integrated classifier system. A classification latency of 3.8 ms per frame is achieved, paving the way for real-time automated threat detection (ATD) for security-screening applications.INDEX TERMS Millimetre-wave, imaging radars, computational imaging, neural networks, image classification, coded-aperture.

show abstract

Section: Coded-aperture CI Systemmentioning

confidence: 99%

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…This has to meet a number of challenging requirements: a large physical aperture, fast switching, operation in the mid-wave infrared etc. The solution adopted is based on a large array of Fabry-Pérot shutters using MOEMS technology [10] as described in a companion paper [23]. By adjusting the coatings on the shutters it was possible to achieve good performance of the shutters (> 90% transmission in the on-state and less than 10% transmission in the off-state) over a 0.5 µm waveband.…”

Section: Persistent Wide Area Surveillance (Pwas)mentioning

confidence: 99%

An adaptive coded aperture imager: building, testing and trialing a super-resolving terrestrial demonstrator

Slinger¹,

Bennett

Dyer³

et al. 2011

SPIE Proceedings

View full text Add to dashboard Cite

There is an increasingly important requirement for day and night, wide field of view imaging and tracking for both imaging and sensing applications. Applications include military, security and remote sensing. We describe the development of a proof of concept demonstrator of an adaptive coded-aperture imager operating in the mid-wave infrared to address these requirements. This consists of a coded-aperture mask, a set of optics and a 4k x 4k focal plane array (FPA). This system can produce images with a resolution better than that achieved by the detector pixel itself (i.e. superresolution) by combining multiple frames of data recorded with different coded-aperture mask patterns. This superresolution capability has been demonstrated both in the laboratory and in imaging of real-world scenes, the highest resolution achieved being ½ the FPA pixel pitch. The resolution for this configuration is currently limited by vibration and theoretically ¼ pixel pitch should be possible. Comparisons have been made between conventional and ACAI solutions to these requirements and show significant advantages in size, weight and cost for the ACAI approach.

show abstract

“…Such waveforms can multiplex a scene information into a set of backscattered measurements, which can be decoded using computational algorithms. Another such example is a coded aperture, in which the transmitted and received wavefronts probing the imaged scene and collecting the back-scattered information are passed through a set of masks with spatio-temporally varying transparency values [29][30][31][32]. Such a modulation can be realized using semiconductor based elements, such as PIN diodes and varactors, to load the radiating elements across the coded aperture and tune their radiation characteristics to create spatio-temporally varying masks [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging

Sharma¹,

Yurduseven²,

Deka³

et al. 2021

PIER B

View full text Add to dashboard Cite

There is an increasing demand in real-time imagery applications such as rapid response to disaster rescue and security screening to name a few. The throughput of a radar imaging system is mainly controlled by two parameters; data acquisition time and signal processing time. To minimize the data acquisition time, various methods are being tried and tested by researchers worldwide. Among them is the computational imaging (CI) technique, which relies on using coded apertures to encode the radar back-scattered measurements onto a set of spatio-temporally incoherent radiation patterns. Such a CI-based imaging approach eliminates the requirement for a raster scan and can substantially simplify the physical hardware architecture. Equally important is the processing time needed to retrieve the scene information from the coded back-scattered measurements. In CI, the simplification in the hardware layer comes at the cost of increased complexity in the signal processing layer due to the indirect mapping and compression of the scene information through the spatio-temporally incoherent transfer function of the coded apertures. To address this particular challenge, this paper presents a hardware-based solution for CI signal processing using a Field Programmable Gate Array (an Xilinx Virtex-7 (XC7VX485T) FPGA chip) architecture. In particular, the proposed method consists of calculating the CI sensing matrix using the FPGA chip and storing it on the FPGA platform for image reconstruction. For the adjoint operation, the calculated sensing matrix is applied on the measured back-scattered waves from the target object. We demonstrate that the FPGA based calculation can reach 21.9 times faster speed than conventional brute-force solutions.

show abstract

Phase-coded aperture for optical imaging

Cited by 27 publications

References 11 publications

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

Coded-Aperture Computational Millimeter-Wave Image Classifier Using Convolutional Neural Network

An adaptive coded aperture imager: building, testing and trialing a super-resolving terrestrial demonstrator

Hardware Enabled Acceleration of Near-Field Coded Aperture Radar Physical Model for Millimetre-Wave Computational Imaging

Contact Info

Product

Resources

About