A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications
2012
IEEE Transactions on Image Processing
In compressive sensing (CS), a challenge is to find a space in which the signal is sparse and, hence, faithfully recoverable. Since many natural signals such as images have locally varying statistics, the sparse space varies in time/spatial domain. As such, CS recovery should be conducted in locally adaptive signal-dependent spaces to counter the fact that the CS measurements are global and irrespective of signal structures. On the contrary, existing CS reconstruction methods use a fixed set of
doi:10.1109/tip.2011.2163520
pmid:21824848
fatcat:gzzux25oobf6dl72hqmdke7j4u