183 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
InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments
Classic designs of hyperspectral instrumentation densely sample the spatial
and spectral information of the scene of interest. Data may be compressed after
the acquisition. In this paper we introduce a framework for the design of an
optimized, micro-patterned snapshot hyperspectral imager that acquires an
optimized subset of the spatial and spectral information in the scene. The data
is thereby compressed already at the sensor level, but can be restored to the
full hyperspectral data cube by the jointly optimized reconstructor. This
framework is implemented with TensorFlow and makes use of its automatic
differentiation for the joint optimization of the layout of the micro-patterned
filter array as well as the reconstructor. We explore the achievable
compression ratio for different numbers of filter passbands, number of scanning
frames, and filter layouts using data collected by the Hyperscout instrument.
We show resulting instrument designs that take snapshot measurements without
losing significant information while reducing the data volume, acquisition
time, or detector space by a factor of 40 as compared to classic, dense
sampling. The joint optimization of a compressive hyperspectral imager design
and the accompanying reconstructor provides an avenue to substantially reduce
the data volume from hyperspectral imagers.Comment: 23 pages, 12 figures, published in Applied Optic
An Overview on Image Forensics
The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes
Plug-and-Play Algorithms for Video Snapshot Compressive Imaging
We consider the reconstruction problem of video snapshot compressive imaging
(SCI), which captures high-speed videos using a low-speed 2D sensor (detector).
The underlying principle of SCI is to modulate sequential high-speed frames
with different masks and then these encoded frames are integrated into a
snapshot on the sensor and thus the sensor can be of low-speed. On one hand,
video SCI enjoys the advantages of low-bandwidth, low-power and low-cost. On
the other hand, applying SCI to large-scale problems (HD or UHD videos) in our
daily life is still challenging and one of the bottlenecks lies in the
reconstruction algorithm. Exiting algorithms are either too slow (iterative
optimization algorithms) or not flexible to the encoding process (deep learning
based end-to-end networks). In this paper, we develop fast and flexible
algorithms for SCI based on the plug-and-play (PnP) framework. In addition to
the PnP-ADMM method, we further propose the PnP-GAP (generalized alternating
projection) algorithm with a lower computational workload. We first employ the
image deep denoising priors to show that PnP can recover a UHD color video with
30 frames from a snapshot measurement. Since videos have strong temporal
correlation, by employing the video deep denoising priors, we achieve a
significant improvement in the results. Furthermore, we extend the proposed PnP
algorithms to the color SCI system using mosaic sensors, where each pixel only
captures the red, green or blue channels. A joint reconstruction and
demosaicing paradigm is developed for flexible and high quality reconstruction
of color video SCI systems. Extensive results on both simulation and real
datasets verify the superiority of our proposed algorithm.Comment: 18 pages, 12 figures and 4 tables. Journal extension of
arXiv:2003.13654. Code available at
https://github.com/liuyang12/PnP-SCI_pytho
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