A Novel Complex-Valued Gaussian Measurement Matrix for Image Compressed Sensing.

Yue Wang, Linlin Xue, Yuqian Yan, Zhongpeng Wang

Open access · goldAbstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2023. 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

1.1field-weighted citation impact, top 28% of its field

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, 5 citations in OpenAlex.

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

4 authors at 1 institution in 1 country.

Yue WangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Linlin XueSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0000-0002-8832-2359

Yuqian YanSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.

Zhongpeng WangSchool of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.ORCID 0000-0002-8236-7054

Zhejiang University of Science and Technology · CN

Funding

National Natural Science Foundation of China 61405178Natural Science Foundation of Zhejiang University of Science and Technology 2023QN110State Key Laboratory of Millimeter Waves, Southeast University, China K202212Zhejiang Provincial Key Natural Science Foundation of China LZ21F010001

6 · The paper itself

Abstract

The measurement matrix used influences the performance of image reconstruction in compressed sensing. To enhance the performance of image reconstruction in compressed sensing, two different Gaussian random matrices were orthogonalized via Gram-Schmidt orthogonalization, respectively. Then, one was used as the real part and the other as the imaginary part to construct a complex-valued Gaussian matrix. Furthermore, we sparsified the proposed measurement matrix to reduce the storage space and computation. The experimental results show that the complex-valued Gaussian matrix after orthogonalization has better image reconstruction performance, and the peak signal-to-noise ratio and structural similarity under different compression ratios are better than the real-valued measurement matrix. Moreover, the sparse measurement matrix can effectively reduce the amount of calculation.

Indexed as

compressed sensingGaussian matrixGram–Schmidt orthogonalizationmeasurement matrixsparse matrix

Identifiers

PMID37761547

DOI10.3390/e25091248

PMCPMC10527653

OpenAlexW4386096468

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.