Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PAT image reconstruction using augmented sparsity regularization with
  practicable tuning of regularization weight   Among all tissue imaging modalities, photo-acoustic tomography (PAT) has been
getting increasing attention in the recent past due to the fact that it has
high contrast, high penetrability, and has capability of retrieving high
resolution. Model-based regularized reconstruction methods used in PAT plays a
crucial role in the applicability of PAT because such methods can yield high
quality images from measurements obtained using fewer number of transducers. A
crucial factor that determines the quality of reconstruction in such methods is
the choice of regularization weight. Unfortunately, an appropriately tuned
value of regularization weight varies significantly with variation in the noise
level, as well as, with the variation in the high resolution contents of the
image, in a way that has not been well understood. There has been attempts to
determine optimum regularization weight from the measured data in the context
of using elementary and general purpose regularizations. In this paper, we
develop a practicable method for semi-automated tuning of the regularization
weight in the context of using a modern type of regularization that is
specifically designed for PAT image reconstruction. As a first step, we
introduce a relative smoothness constraint with a parameter; this parameter
computationally maps into the actual regularization weight, but, its tuning
does not vary significantly with variation in the noise level, and with the
variation in the high resolution contents of the image. Next, we construct an
algorithm that integrates the task of determining this mapping along with
obtaining the reconstruction. Finally we demonstrate experimentally that we can
run this algorithm with a nominal value of the relative smoothness parameter --
a value independent of the noise level and the structure of the underlying
image -- to obtain good quality reconstructions.

15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas