120 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
Discriminative Transfer Learning for General Image Restoration
Recently, several discriminative learning approaches have been proposed for
effective image restoration, achieving convincing trade-off between image
quality and computational efficiency. However, these methods require separate
training for each restoration task (e.g., denoising, deblurring, demosaicing)
and problem condition (e.g., noise level of input images). This makes it
time-consuming and difficult to encompass all tasks and conditions during
training. In this paper, we propose a discriminative transfer learning method
that incorporates formal proximal optimization and discriminative learning for
general image restoration. The method requires a single-pass training and
allows for reuse across various problems and conditions while achieving an
efficiency comparable to previous discriminative approaches. Furthermore, after
being trained, our model can be easily transferred to new likelihood terms to
solve untrained tasks, or be combined with existing priors to further improve
image restoration quality
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
Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method
Novel optical imaging devices allow for hybrid acquisition modalities such as
compressed acquisitions with locally different spatial and spectral resolutions
captured by a single focal plane array. In this work, we propose to model the
capturing system of a multiresolution coded acquisition (MRCA) in a unified
framework, which natively includes conventional systems such as those based on
spectral/color filter arrays, compressed coded apertures, and multiresolution
sensing. We also propose a model-based image reconstruction algorithm
performing a joint demosaicing and fusion (JoDeFu) of any acquisition modeled
in the MRCA framework. The JoDeFu reconstruction algorithm solves an inverse
problem with a proximal splitting technique and is able to reconstruct an
uncompressed image datacube at the highest available spatial and spectral
resolution. An implementation of the code is available at
https://github.com/danaroth83/jodefu.Comment: 15 pages, 7 figures; regular pape
Color Image Reconstruction via Sparse Signal Representation
In una macchina fotografica digitale, ogni unità fotosensibile misura solo una delle tre componenti di colore che la rappresentazione di un'immagine digitale a colori richiede. L'operazione di ricostruzione delle componenti mancanti è nota come demosaicing. In questa tesi si e' studiato e implementato un algoritmo di demosaicing recentemente proposto in letteratura, e basato sulla rappresentazione sparsa, attraverso un dizionario, delle immagini naturali acquisite dalla macchina fotografic
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