Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models   Sparsity and low-rank models have been popular for reconstructing images and
videos from limited or corrupted measurements. Dictionary or transform learning
methods are useful in applications such as denoising, inpainting, and medical
image reconstruction. This paper proposes a framework for online (or
time-sequential) adaptive reconstruction of dynamic image sequences from linear
(typically undersampled) measurements. We model the spatiotemporal patches of
the underlying dynamic image sequence as sparse in a dictionary, and we
simultaneously estimate the dictionary and the images sequentially from
streaming measurements. Multiple constraints on the adapted dictionary are also
considered such as a unitary matrix, or low-rank dictionary atoms that provide
additional efficiency or robustness. The proposed online algorithms are memory
efficient and involve simple updates of the dictionary atoms, sparse
coefficients, and images. Numerical experiments demonstrate the usefulness of
the proposed methods in inverse problems such as video reconstruction or
inpainting from noisy, subsampled pixels, and dynamic magnetic resonance image
reconstruction from very limited measurements.

20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an