300 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
DESIGN OF CMOS COMPRESSIVE SENSING IMAGE SENSORS
This work investigates the optimal measurement matrices that can be used in compressive sensing (CS) image sensors. It also optimizes CMOS current-model pixel cell circuits for CS image sensors. Based on the outcomes from these optimization studies, three CS image senor circuits with compression ratios of 4, 6, and 8 are designed with using a 130 nm CMOS technology. The pixel arrays used in the image sensors has a size of 256X256. Circuit simulations with benchmark image Lenna show that the three images sensors can achieve peak signal to noise ratio (PSNR) values of 37.64, 33.29, and 32.44 dB respectively
Design of Scalable Hardware-Efficient Compressive Sensing Image Sensors
This work presents a new compressive sensing (CS) measurement method for image sensors, which limits pixel summation within neighbor pixels and follows regular summation patterns. Simulations with a large set of benchmark images show that the proposed method leads to improved image quality. Circuit implementation for the proposed CS measurement method is presented with the use of current mode pixel cells; and the resultant CS image sensor circuit is significantly simpler than existing designs. With compression rates of 4 and 8, the developed CS image sensors can achieve 34.2 dB and 29.6 dB PSNR values with energy consumption of 1.4 mJ and 0.73 mJ per frame, respectively
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Block-based feature adaptive compressive sensing for video
This paper focuses on the problem of feature adaptive reconstruction of Compressive Sensing (CS) captured video. In CS, sparse signals can be recovered with high probability of success from very few random samples. Utilizing the temporal correlations between video frames, it is possible to exploit improved CS reconstruction algorithms. Features that relate to the changes between frames are one of the options to benefit reconstruction. However, to choose the optimal feature for every particular region in each frame is difficult, as the true images are unknown in a CS framework. In this paper, we propose two systems for block-based feature adaptive CS video reconstruction, i.e., a Cross Validation (CV) based system and a classification based system. The CV based system achieves the selection of the optimal feature by applying the techniques of CV to the results of extra reconstructions and the classification based system reduces complexity by classifying the CS samples directly, where the optimal feature for the particular class is employed for the reconstruction. Simulations demonstrate that both of our systems work appropriately and their performance is better than uniformly using any single feature for the whole video reconstruction.This work is supported by EPSRC Research Grant (EP/K033700/1); the Natural Science Foundation of China (61401018); Beijing Jiaotong University; the Fundamental Research Funds for the Central Universities (2014JBM149).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.25
Structured random measurements in signal processing
Compressed sensing and its extensions have recently triggered interest in
randomized signal acquisition. A key finding is that random measurements
provide sparse signal reconstruction guarantees for efficient and stable
algorithms with a minimal number of samples. While this was first shown for
(unstructured) Gaussian random measurement matrices, applications require
certain structure of the measurements leading to structured random measurement
matrices. Near optimal recovery guarantees for such structured measurements
have been developed over the past years in a variety of contexts. This article
surveys the theory in three scenarios: compressed sensing (sparse recovery),
low rank matrix recovery, and phaseless estimation. The random measurement
matrices to be considered include random partial Fourier matrices, partial
random circulant matrices (subsampled convolutions), matrix completion, and
phase estimation from magnitudes of Fourier type measurements. The article
concludes with a brief discussion of the mathematical techniques for the
analysis of such structured random measurements.Comment: 22 pages, 2 figure
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