Name
Abstract | ||
|---|---|---|
Remote sensing and astronomical image formation is often complicated by deficiencies in measurement quality, density, or diversity. Penalized likelihood methods can incorporate additional first-principles physical prior knowledge and improve the image reconstructions, but a systematic bias is unavoidable as a consequence. This work derives theory to understand the bias and develops a computational tool to probe its effect on the reconstructed image and bound resolution limits. Though the focus is on image formation, the contributions of this paper apply to any inference problem that can be expressed under the linear state-space signal model. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIP.2011.6116261 | 2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) |
Keywords | Field | DocType |
remote sensing, multidimensional signal processing, recursive estimation, Kalman filter | Iterative reconstruction,Computer vision,Signal processing,Multidimensional signal processing,Pattern recognition,Computer science,Inference,Image processing,Image formation,Kalman filter,Artificial intelligence,Image resolution | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
0 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark D. Butala | 1 | 24 | 4.80 |