Evidence map›Paper›PMID 30360433›Full record

ArticleSensors (Basel, Switzerland)2018

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model.

Shiping Ma, Hongqiang Ma, Yuelei Xu, Shuai Li, Chao Lv, Mingming Zhu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 11% 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

7 citing papers in PubMed, 35 citations in OpenAlex.

  1. Quantitative skin surface hydration measurement by visible optical image processing: A pilot study.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024
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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

6 authors at 2 institutions in 1 country.

Shiping MaAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. mashiping@126.com.
Hongqiang MaAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. 18049025189@163.com.
Yuelei XuAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. yuelei_xu@163.com.
Shuai LiAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. lishuailishuai@163.com.ORCID 0000-0002-7562-9220
Chao LvAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. lvchao1112@163.com.
Mingming ZhuAeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China. ming_paper@163.com.
Air Force Engineering University · CNNorthwestern Polytechnical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (

Indexed as

batch normalizationcolor modelconvolutional neural networkfeature learningimage enhancementRetinex model

Identifiers

PMID30360433
PMCPMC6209959
OpenAlexW2897069152

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.