Compressive Sensing for Background Subtraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Analyzing Least Squares and Kalman Filtered Compressed Sensing
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Modified-CS: modifying compressive sensing for problems with partially known support
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Modified-CS: modifying compressive sensing for problems with partially known support
IEEE Transactions on Signal Processing
Block-Based Compressed Sensing of Images and Video
Foundations and Trends in Signal Processing
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In this work, we propose algorithms to recursively and causally reconstruct a sequence of natural images from a reduced number of linear projection measurements taken in a domain that is "incoherent" with respect to the image's sparsity basis (typically wavelet) and demonstrate their application in real-time MR image reconstruction. For a static version of the above problem, Compressed Sensing (CS) provides a provably exact and computationally efficient solution. But most existing solutions for the actual problem are either offline and non-causal or cannot compute an exact reconstruction (for truly sparse signal sequences), except using as many measurements as those needed for CS. The key idea of our proposed solution (modified-CS) is to design a modification of CS when a part of the support set is known (available from reconstructing the previous image). We demonstrate the exact reconstruction property of modified-CS on full-size image sequences using much fewer measurements than those required for CS. Greatly improved performance over existing work is demonstrated for approximately sparse signals or noisy measurements.