 
Summary: 1
Adaptive Compressed Image Sensing Using Dictionaries
Amir Averbuch*, Shai Dekel** and Shay Deutsch*
Abstract. In recent years, the theory of Compressed Sensing has emerged as an alternative for the Shannon
sampling theorem, suggesting that compressible signals can be reconstructed from far fewer samples than
required by the Shannon sampling theorem. In fact the theory advocates that nonadaptive, `random' functionals
are in some sense optimal for this task. However, in practice Compressed Sensing is very difficult to implement
for large data sets, since the algorithms are exceptionally slow and have high memory consumption. In this
work, we present a new alternative method for simultaneous image acquisition and compression called
Adaptive Compressed Sampling. Our approach departs fundamentally from the (non adaptive) Compressed
Sensing mathematical framework by replacing the `universal' acquisition of incoherent measurements with a
direct and fast method for adaptive transform coefficient acquisition. The main advantages of this direct
approach are that no complex recovery algorithm is in fact needed and that it allows more control over the
compressed image quality, in particular, the sharpness of edges. Our experimental results show that our adaptive
algorithms perform better than existing nonadaptive methods in terms of image quality and speed.
* TelAviv University
** GE Healthcare and TelAviv University
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1 Introduction
Equation Section 1
