Evidence map›Paper›PMID 36560332›Full record

ArticleSensors (Basel, Switzerland)2022

Learning to Sense for Coded Diffraction Imaging.

Rakib Hyder, Zikui Cai, M Salman Asif

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Rakib HyderDepartment of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA.ORCID 0000-0003-4191-301X
Zikui CaiDepartment of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA.ORCID 0000-0003-1663-9493
M Salman AsifDepartment of Electrical and Computer Engineering, University of California, Riverside, CA 92521, USA.ORCID 0000-0001-5993-3903

Funding

National Science Foundation CCF-2046293
6 · The paper itself

Abstract

In this paper, we present a framework to learn illumination patterns to improve the quality of signal recovery for coded diffraction imaging. We use an alternating minimization-based phase retrieval method with a fixed number of iterations as the iterative method. We represent the iterative phase retrieval method as an unrolled network with a fixed number of layers where each layer of the network corresponds to a single step of iteration, and we minimize the recovery error by optimizing over the illumination patterns. Since the number of iterations/layers is fixed, the recovery has a fixed computational cost. Extensive experimental results on a variety of datasets demonstrate that our proposed method significantly improves the quality of image reconstruction at a fixed computational cost with illumination patterns learned only using a small number of training images.

Indexed as

AlgorithmsDiagnostic ImagingImage Processing, Computer-Assistedcoded diffraction imaginglearned sensorsphase retrieval

Identifiers

PMID36560332
PMCPMC9788068

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.