# "On compressed sensing image reconstruction using linear prediction in adaptive filtering"

Title | "On compressed sensing image reconstruction using linear prediction in adaptive filtering" |

Publication Type | Conference Paper |

Year of Publication | 2015 |

Authors | S. R. Islam, S. P. Maity, A. K. Ray |

Conference Name | 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) |

Date Published | Aug. 2015 |

Publisher | IEEE |

ISBN Number | 978-1-4799-8792-4 |

Accession Number | 15486910 |

Keywords | adaptive filter, adaptive filtering, adaptive filters, Approximation algorithms, Approximation methods, compressed sensing, compressive sampling, CS reconstruction problem, distance based linear prediction, Image reconstruction, magnetic resonance imaging, Modified-RM approximation, MRI, Noise, nonparametric approach, Nyquist rate requirement, prediction, Prediction algorithms, pubcrawl170104, random noise, Robbins-Monro stochastic approximation, Sensors, Sparse matrices, sparsity, spatial domain adaptive Wiener filter, Stochastic processes, Wiener filters |

Abstract | Compressed sensing (CS) or compressive sampling deals with reconstruction of signals from limited observations/ measurements far below the Nyquist rate requirement. This is essential in many practical imaging system as sampling at Nyquist rate may not always be possible due to limited storage facility, slow sampling rate or the measurements are extremely expensive e.g. magnetic resonance imaging (MRI). Mathematically, CS addresses the problem for finding out the root of an unknown distribution comprises of unknown as well as known observations. Robbins-Monro (RM) stochastic approximation, a non-parametric approach, is explored here as a solution to CS reconstruction problem. A distance based linear prediction using the observed measurements is done to obtain the unobserved samples followed by random noise addition to act as residual (prediction error). A spatial domain adaptive Wiener filter is then used to diminish the noise and to reveal the new features from the degraded observations. Extensive simulation results highlight the relative performance gain over the existing work. |

URL | https://ieeexplore.ieee.org/document/7275964 |

DOI | 10.1109/ICACCI.2015.7275964 |

Citation Key | 7275964 |

- Noise
- Wiener filters
- Stochastic processes
- spatial domain adaptive Wiener filter
- sparsity
- Sparse matrices
- sensors
- Robbins-Monro stochastic approximation
- random noise
- pubcrawl170104
- Prediction algorithms
- prediction
- Nyquist rate requirement
- nonparametric approach
- adaptive filter
- MRI
- Modified-RM approximation
- magnetic resonance imaging
- Image reconstruction
- distance based linear prediction
- CS reconstruction problem
- compressive sampling
- compressed sensing
- Approximation methods
- Approximation algorithms
- adaptive filters
- adaptive filtering