Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improved Total Variation based Image Compressive Sensing Recovery by
  Nonlocal Regularization   Recently, total variation (TV) based minimization algorithms have achieved
great success in compressive sensing (CS) recovery for natural images due to
its virtue of preserving edges. However, the use of TV is not able to recover
the fine details and textures, and often suffers from undesirable staircase
artifact. To reduce these effects, this letter presents an improved TV based
image CS recovery algorithm by introducing a new nonlocal regularization
constraint into CS optimization problem. The nonlocal regularization is built
on the well known nonlocal means (NLM) filtering and takes advantage of
self-similarity in images, which helps to suppress the staircase effect and
restore the fine details. Furthermore, an efficient augmented Lagrangian based
algorithm is developed to solve the above combined TV and nonlocal
regularization constrained problem. Experimental results demonstrate that the
proposed algorithm achieves significant performance improvements over the
state-of-the-art TV based algorithm in both PSNR and visual perception.

20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ