Intensity modulation-based spectral polarization measurement method of coded aperture

Qiao, Ya; Ning, Jing; Zhang, Rui; Wang, Zhibin; Li, Jinyu

doi:10.1016/j.optcom.2018.12.061

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…This limits its use for large scale scenes because of the expensive computational memory requirements and the potential overfitting problems [64]. We propose to reduce the number of trainable parameters by adding spatial structure to each CA so that a kernel Q s of size ∆ n × ∆ m N × M is periodically repeated as follows Number of snapshots in (10) Trainable parameters in (14) manufacturing noise in (16) Conditionality in (13) Multishot correlation in (12) 4. Regularizer Fig.…”

Section: F Data Driven Conditionality Constraintmentioning

confidence: 99%

“…This numerical test aims to demonstrate the importance of reducing the number of trainable parameters to avoid overffiting problems in the E2E approach by using the CA structure strategy presented in (14). For this, we select the CASSI system, with 5 < (L = 31) optical filters in the Colored-CA, for the reconstruction task.…”

Section: G Number Of Trainable Parameters Experimentsmentioning

confidence: 99%

“…Analytically, the CA is modeled as a tensor array where each spatial location has a particular response to the incoming wavefront. According to the nature of the entry values they can be found polarizer CA [4], complex-modulating CA [10], phase CA [13], intensity-modulating CA [14], among others. This work focuses on CAs modulating the intensity through (i) binary CA (BCA) [15], with opaque and translucent elements that completely block or let pass the wavefront, and (ii) realvalued CA [16] that attenuates the wavefront at different levels.…”

Section: Introductionmentioning

confidence: 99%

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Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Bacca¹,

Gelvez²,

Argüello³

2021

Preprint

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Covering from photography to depth and spectral estimation, diverse computational imaging (CI) applications benefit from the versatile modulation of coded apertures (CAs). The light wave fields as space, time, or spectral can be modulated to obtain projected encoded information at the sensor that is then decoded by efficient methods, such as the modern deep learning decoders. Despite the CA can be fabricated to produce an analog modulation, a binary CA is mostly preferred since easier calibration, higher speed, and lower storage are achieved. As the performance of the decoder mainly depends on the structure of the CA, several works optimize the CA ensembles by customizing regularizers for a particular application without considering critical physical constraints of the CAs. This work presents an end-to-end (E2E) deep learning-based optimization of CAs for CI tasks. The CA design method aims to cover a wide range of CI problems easily changing the loss function of the deep approach. The designed loss function includes regularizers to fulfill the widely used sensing requirements of the CI applications. Mainly, the regularizers can be selected to optimize the transmittance, the compression ratio and the correlation between measurements, while a binary CA solution is encouraged, and the performance of the CI task is maximized in applications such as restoration, classification, and semantic segmentation.

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Section: F Data Driven Conditionality Constraintmentioning

confidence: 99%

Section: G Number Of Trainable Parameters Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Bacca¹,

Gelvez²,

Argüello³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The spectropolarimetric resolution recovered from the CS and CSIM retains the intrinsic spectral resolution of the imaging spectrometer used. This point is very important for the imaging spectrometers that only have modest spectral resolution 12 , 19 , 31 , 32 . However, the FTM, CS, and CSIM methods are still subject to noise perturbation, since the SPI systems usually have lower flux due to a narrow spectral band and the use of a polarizer.…”

Section: Introductionmentioning

confidence: 99%

“…This point is very important for the imaging spectrometers that only have modest spectral resolution. 12,19,31,32 However, the FTM, CS, and CSIM methods are still subject to noise perturbation, since the SPI systems usually have lower flux due to a narrow spectral band and the use of a polarizer. In addition, the FTM and CS methods are enslaved to systematic errors originating from double MORs.…”

Section: Introductionmentioning

confidence: 99%

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Han

et al. 2023

Adv. Photon. Nexus

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Compressive full-Stokes spectropolarimetric imaging (SPI), integrating passive polarization modulator (PM) into general imaging spectrometer, is powerful enough to capture high-dimensional information via incomplete measurement; a reconstruction algorithm is needed to recover 3D data cube (x , y , and λ) for each Stokes parameter. However, existing PMs usually consist of complex elements and enslave to accurate polarization calibration, current algorithms suffer from poor imaging quality and are subject to noise perturbation. In this work, we present a single multiple-order retarder followed a polarizer to implement passive spectropolarimetric modulation. After building a unified forward imaging model for SPI, we propose a deep image prior plus sparsity prior algorithm for high-quality reconstruction. The method based on untrained network does not need training data or accurate polarization calibration and can simultaneously reconstruct the 3D data cube and achieve self-calibration. Furthermore, we integrate the simplest PM into our miniature snapshot imaging spectrometer to form a single-shot SPI prototype. Both simulations and experiments verify the feasibility and outperformance of our SPI scheme. It provides a paradigm that allows general spectral imaging systems to become passive full-Stokes SPI systems by integrating the simplest PM without changing their intrinsic mechanism.

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Template-confined growth of copper halides micro-wire arrays for highly polarization-sensitive deep-ultraviolet photodetectors with a polarization sensitivity of 4.45

Liang

et al. 2023

Chemical Engineering Journal

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Intensity modulation-based spectral polarization measurement method of coded aperture

Cited by 16 publications

References 20 publications

Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Deep Coded Aperture Design: An End-to-End Approach for Computational Imaging Tasks

Deep image prior plus sparsity prior: toward single-shot full-Stokes spectropolarimetric imaging with a multiple-order retarder

Template-confined growth of copper halides micro-wire arrays for highly polarization-sensitive deep-ultraviolet photodetectors with a polarization sensitivity of 4.45

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