Adaptive Memory-Augmented Unfolding Network for Compressed Sensing.

Mingkun Feng, Dongcan Ning, Shengying Yang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mingkun FengSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0000-0001-6716-8949

Dongcan NingSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Shengying YangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0000-0003-3010-1622

Funding

the Basic Public Welfare Research Program of Zhejiang Province under Grant No. LGF22F020017Zhejiang Provincial Key Research and Development Project Project No. 2024C01135

6 · The paper itself

Abstract

Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS). Concretely, without loss of interpretability, we integrate an adaptive content-aware strategy into the gradient descent step of the proximal gradient descent (PGD) algorithm, driving it to adaptively capture the adequate features. In addition, we extended AMAUN-CS based on the memory storage mechanism of the human brain and propose AMAUN-CS

Indexed as

compressed sensingdeep unrollingimage reconstructionneural networksproximal gradient descent

Identifiers

PMID39771820

DOI10.3390/s24248085

PMCPMC11679078

What OpenQuestion holds

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Registered trials

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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.