663 research outputs found
Reconstruction of compressed spectral imaging based on global structure and spectral correlation
In this paper, a convolution sparse coding method based on global structure
characteristics and spectral correlation is proposed for the reconstruction of
compressive spectral images. The proposed method uses the convolution kernel to
operate the global image, which can better preserve image structure information
in the spatial dimension. To take full exploration of the constraints between
spectra, the coefficients corresponding to the convolution kernel are
constrained by the norm to improve spectral accuracy. And, to solve the problem
that convolutional sparse coding is insensitive to low frequency, the global
total-variation (TV) constraint is added to estimate the low-frequency
components. It not only ensures the effective estimation of the low-frequency
but also transforms the convolutional sparse coding into a de-noising process,
which makes the reconstructing process simpler. Simulations show that compared
with the current mainstream optimization methods (DeSCI and Gap-TV), the
proposed method improves the reconstruction quality by up to 7 dB in PSNR and
10% in SSIM, and has a great improvement in the details of the reconstructed
image
High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction
Spectral imaging is a fundamental diagnostic technique with widespread
application. Conventional spectral imaging approaches have intrinsic
limitations on spatial and spectral resolutions due to the physical components
they rely on. To overcome these physical limitations, in this paper, we develop
a novel multi-spectral imaging modality that enables higher spatial and
spectral resolutions. In the developed computational imaging modality, we
exploit a diffractive lens, such as a photon sieve, for both dispersing and
focusing the optical field, and achieve measurement diversity by changing the
focusing behavior of this lens. Because the focal length of a diffractive lens
is wavelength-dependent, each measurement is a superposition of differently
blurred spectral components. To reconstruct the individual spectral images from
these superimposed and blurred measurements, model-based fast reconstruction
algorithms are developed with deep and analytical priors using alternating
minimization and unrolling. Finally, the effectiveness and performance of the
developed technique is illustrated for an application in astrophysical imaging
under various observation scenarios in the extreme ultraviolet (EUV) regime.
The results demonstrate that the technique provides not only
diffraction-limited high spatial resolution, as enabled by diffractive lenses,
but also the capability of resolving close-by spectral sources that would not
otherwise be possible with the existing techniques. This work enables high
resolution multi-spectral imaging with low cost designs for a variety of
applications and spectral regimes.Comment: accepted for publication in IEEE Transactions on Computational
Imaging, see DOI belo
CalibFPA: A Focal Plane Array Imaging System based on Online Deep-Learning Calibration
Compressive focal plane arrays (FPA) enable cost-effective high-resolution
(HR) imaging by acquisition of several multiplexed measurements on a
low-resolution (LR) sensor. Multiplexed encoding of the visual scene is
typically performed via electronically controllable spatial light modulators
(SLM). An HR image is then reconstructed from the encoded measurements by
solving an inverse problem that involves the forward model of the imaging
system. To capture system non-idealities such as optical aberrations, a
mainstream approach is to conduct an offline calibration scan to measure the
system response for a point source at each spatial location on the imaging
grid. However, it is challenging to run calibration scans when using structured
SLMs as they cannot encode individual grid locations. In this study, we propose
a novel compressive FPA system based on online deep-learning calibration of
multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that
locomotes a pre-printed fixed coded aperture. A deep neural network is then
leveraged to correct for the influences of system non-idealities in multiplexed
measurements without the need for offline calibration scans. Finally, a deep
plug-and-play algorithm is used to reconstruct images from corrected
measurements. On simulated and experimental datasets, we demonstrate that
CalibFPA outperforms state-of-the-art compressive FPA methods. We also report
analyses to validate the design elements in CalibFPA and assess computational
complexity
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
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