Extensions of compressed sensing
Signal Processing - Sparse approximations in signal and image processing
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
A Frame Construction and a Universal Distortion Bound for Sparse Representations
IEEE Transactions on Signal Processing
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Joint Source–Channel Communication for Distributed Estimation in Sensor Networks
IEEE Transactions on Information Theory
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Compressive sensing(CS) has inspired significant interest because of its compressive capability and lack of complexity on the sensor side. In this paper, we present a study of three sampling patterns and investigate their performance on CS reconstruction. We then propose a new image fusion algorithm in the compressive domain by using an improved sampling pattern. There are few studies regarding the applicability of CS to image fusion. The main purpose of this work is to explore the properties of compressive measurements through different sampling patterns and their potential use in image fusion. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the multiresolution (MR) domain. The simulations show that the proposed CS-based image fusion algorithm provides promising results.