Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Phase retrieval with physics informed zero-shot learning   Phase can be reliably estimated from a single diffracted intensity image, if
a faithful prior information about the object is available. Examples include
amplitude bounds, object support, sparsity in the spatial or a transform
domain, deep image prior and the prior learnt from the labelled datasets by a
deep neural network. Deep learning facilitates state of art reconstruction
quality but requires a large labelled dataset (ground truth-measurement pair
acquired in the same experimental conditions) for training. To alleviate this
data requirement problem, this letter proposes a zero-shot learning method. The
letter demonstrates that the object-prior learnt by a deep neural network while
being trained for a denoising task can also be utilized for the phase
retrieval, if the diffraction physics is effectively enforced on the network
output. The letter additionally demonstrates that the incorporation of total
variation in the proposed zero-shot framework facilitates the reconstruction of
similar quality in lesser time (e.g. ~8.5 fold, for a test reported in this
letter).

28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
1
The categories of this paper are: cs
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg