Evidence map›Paper›PMID 39123136›Full record

ArticleBMC medical imaging2024

Retinex theory-based nonlinear luminance enhancement and denoising for low-light endoscopic images.

En Mou, Huiqian Wang, Xiaodong Chen, Zhangyong Li, Enling Cao, Yuanyuan Chen, Zhiwei Huang, Yu Pang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2024. 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

8 authors.

En MouSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Huiqian WangSchool of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. wanghq@cqupt.edu.cn.
Xiaodong ChenSchool of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China.
Zhangyong LiSchool of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Enling CaoSchool of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Yuanyuan ChenSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
Zhiwei HuangSchool of Medical Information and Engineering, Southwest Medical University, Luzhou, 646000, China.
Yu PangSchool of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China. pangyu@cqupt.edu.cn.

Funding

China Postdoctoral Science Foundation 2022MD713702Chongqing Natural Science Foundation Innovation and Development Joint Fund Project CSTB2023NSCQ-LZX0064Chongqing Research Program of Technological Innovation and Application Demonstration CSTB2022TIAD-KPX0062National Natural Science Foundation of China U21A20447Research Project of Southwest Medical University 2021ZKZD019Science and Technology Research Program of Chongqing Municipal Education Commission KJQN202100602Sichuan Province Science and Technology Support Program 2022NSFSC0508Special Postdoctoral Support from Chongqing Human Resources and Social Security Bureau 2021XM3010
6 · The paper itself

Abstract

backgroundThe quality of low-light endoscopic images involves applications in medical disciplines such as physiology and anatomy for the identification and judgement of tissue structures. Due to the use of point light sources and the constraints of narrow physiological structures, medical endoscopic images display uneven brightness, low contrast, and a lack of texture information, presenting diagnostic challenges for physicians.

methodsIn this paper, a nonlinear brightness enhancement and denoising network based on Retinex theory is designed to improve the brightness and details of low-light endoscopic images. The nonlinear luminance enhancement module uses higher-order curvilinear functions to improve overall brightness; the dual-attention denoising module captures detailed features of anatomical structures; and the color loss function mitigates color distortion.

resultsExperimental results on the Endo4IE dataset demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS). The PSNR is 27.2202, SSIM is 0.8342, and the LPIPS is 0.1492. It provides a method to enhance image quality in clinical diagnosis and treatment.

conclusionsIt offers an efficient method to enhance images captured by endoscopes and offers valuable insights into intricate human physiological structures, which can effectively assist clinical diagnosis and treatment.

Indexed as

Signal-To-Noise RatioAlgorithmsEndoscopyHumansImage EnhancementImage Processing, Computer-AssistedNonlinear DynamicsDenoisingLow-light endoscopic imagesNonlinear luminance enhancementRetinex theory

Identifiers

PMID39123136
PMCPMC11316405

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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.