<p>This thesis concerns with recovery of compressive sampled images. Since many natural signals such as images are non-stationary, the sparse space varies in time/spatial domain. Therefore, compressive sensing (CS) recovery should be carried on locally adaptive, signal-dependent spaces to answer the fact that the CS measurements are not dependant to the signal structures. Existing CS reconstruction algorithms use fixed basis such as wavelets and DCT for the signals. To address this problem, we proposed new technique for model guided adaptive recovery of compressive sensing. The proposed algorithms are based on two dimensional piecewise autoregressive model and can adaptively recover compressive sampled images. In addition, proposed algorithms offer a powerful mechanism to characterize structured sparsity of natural images. This mechanism greatly restricts the CS solution space. Simulation results show the preeminent effect of our algorithms in the recovery of wide range of natural images. In average our best algorithm improves the reconstruction quality of existing CS methods by 2dB.</p> / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/9148 |
| Date | 09 1900 |
| Creators | Pournaghi, Reza |
| Contributors | Wu, Xiaolin, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Detected Language | English |
| Type | thesis |
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