3,856 research outputs found
Approximate Message Passing in Coded Aperture Snapshot Spectral Imaging
We consider a compressive hyperspectral imaging reconstruction problem, where
three-dimensional spatio-spectral information about a scene is sensed by a
coded aperture snapshot spectral imager (CASSI). The approximate message
passing (AMP) framework is utilized to reconstruct hyperspectral images from
CASSI measurements, and an adaptive Wiener filter is employed as a
three-dimensional image denoiser within AMP. We call our algorithm
"AMP-3D-Wiener." The simulation results show that AMP-3D-Wiener outperforms
existing widely-used algorithms such as gradient projection for sparse
reconstruction (GPSR) and two-step iterative shrinkage/thresholding (TwIST)
given the same amount of runtime. Moreover, in contrast to GPSR and TwIST,
AMP-3D-Wiener need not tune any parameters, which simplifies the reconstruction
process.Comment: to appear in Globalsip 201
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing
(CS) for video. We propose a CS algorithm to adapt the compression ratio based
on the scene's temporal complexity, computed from the compressed data, without
compromising the quality of the reconstructed video. The temporal adaptivity is
manifested by manipulating the integration time of the camera, opening the
possibility to real-time implementation. The proposed algorithm is a
generalized temporal CS approach that can be incorporated with a diverse set of
existing hardware systems.Comment: IEEE Interonal International Conference on Image Processing
(ICIP),201
The Application of Preconditioned Alternating Direction Method of Multipliers in Depth from Focal Stack
Post capture refocusing effect in smartphone cameras is achievable by using
focal stacks. However, the accuracy of this effect is totally dependent on the
combination of the depth layers in the stack. The accuracy of the extended
depth of field effect in this application can be improved significantly by
computing an accurate depth map which has been an open issue for decades. To
tackle this issue, in this paper, a framework is proposed based on
Preconditioned Alternating Direction Method of Multipliers (PADMM) for depth
from the focal stack and synthetic defocus application. In addition to its
ability to provide high structural accuracy and occlusion handling, the
optimization function of the proposed method can, in fact, converge faster and
better than state of the art methods. The evaluation has been done on 21 sets
of focal stacks and the optimization function has been compared against 5 other
methods. Preliminary results indicate that the proposed method has a better
performance in terms of structural accuracy and optimization in comparison to
the current state of the art methods.Comment: 15 pages, 8 figure
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