M.Sc Student | Fidelman Peli |
---|---|
Subject | Power Law Base Adaptive Compressive Sensing |
Department | Department of Electrical Engineering | Supervisor | Professor Emeritus Arie Feuer |
Full Thesis text | ![]() |
In this paper we present a novel Adaptive Compressive
Sensing (ACS) for natural images, named M-ACS (Modified Adaptive Compressive
Sensing). Using our
algorithm
an image is divided into blocks and a Discrete Cosine Transform (DCT)
coefficients
of each block are then selectively calculated (sampled) directly from the
raw
data. Our ACS algorithm uses a novel prediction mechanism to select which
coefficients
should be sampled, based on a novel stochastic power-law model for the
distribution
of DCT coefficients and a uniquely designed noise estimation method.
As
opposed to standard Compressive Sensing (CS), the number of coefficients to be
sampled
is not predetermined and is not identical for all blocks. The underlying
assumption we use is a power-law model for the distribution of the DCT
coefficients
in
each block. Several coefficients are sampled and used to estimate the block distribution
model, which is then used to determine if additional samples are needed
to
achieve a desired image quality. Unlike many research papers in the field which
test
their algorithms on processed images, we do all our tests using raw image data.
We
demonstrate through several examples, the performance advantage of our ACS
over
standard CS algorithms, both in reconstructed image quality, bandwidth and
reconstruction computational simplicity. Using our algorithm a reduction in the
power
consumption
required by an imaging device can be achieved.