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A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING

Abstract: 

Recently, multidimensional signal reconstruction using a low number of measurements is of great interest. Therefore, an effective sampling scheme which should acquire the most information of signal using a low number of measurements is required. In this paper, we study a novel cube-based method for sampling and reconstruction of multidimensional signals. First, inspired by the block-based compressive sensing (BCS), we divide a group of pictures (GoP) in a video sequence into cubes. By this way, we can easily store the measurement matrix and also easily can generate the sparsifying basis. The reconstruction process also can be done in parallel. Second, along with the Kronecker structure of the sampling matrix, we design a weight matrix based on the human visuality system, i.e. perceptually. We will also benefit from different weighted l1-minimization methods for reconstruction. Furthermore, conventional methods for BCS consider an equal number of samples for all blocks. However, the sparsity order of blocks in natural images could be different and, therefore, a various number of samples could be required for their reconstruction. Motivated by this point, we will adaptively allocate the samples for each cube in a video sequence.
Our aim is to show that our simple linear sampling approach can be competitive with the other state-of-the-art methods.

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Paper Details

Authors:
Seyed Hamid Safavi, Farah Torkamani-Azar
Submitted On:
12 March 2017 - 11:44am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Seyed Hamid Safavi
Paper Code:
ICASSP1701
Document Year:
2017
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(71 downloads)

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[1] Seyed Hamid Safavi, Farah Torkamani-Azar, "A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1748. Accessed: Aug. 24, 2017.
@article{1748-17,
url = {http://sigport.org/1748},
author = {Seyed Hamid Safavi; Farah Torkamani-Azar },
publisher = {IEEE SigPort},
title = {A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING},
year = {2017} }
TY - EJOUR
T1 - A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING
AU - Seyed Hamid Safavi; Farah Torkamani-Azar
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1748
ER -
Seyed Hamid Safavi, Farah Torkamani-Azar. (2017). A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING. IEEE SigPort. http://sigport.org/1748
Seyed Hamid Safavi, Farah Torkamani-Azar, 2017. A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING. Available at: http://sigport.org/1748.
Seyed Hamid Safavi, Farah Torkamani-Azar. (2017). "A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING." Web.
1. Seyed Hamid Safavi, Farah Torkamani-Azar. A NOVEL ADAPTIVE WEIGHTED KRONECKER COMPRESSIVE SENSING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1748