Evidence map›Paper›PMID 40942781›Full record

ArticleSensors (Basel, Switzerland)2025

DLFE-Net: Preserving Details and Removing Noise Using HVI Color Space for Low-Light Image Enhancement.

Zhaokun He, Xin Yuan, Guozhu Hao, Wei Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

4 authors.

Zhaokun HeSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.ORCID 0009-0009-4843-6309
Xin YuanSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.ORCID 0000-0003-3140-3243
Guozhu HaoHubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China.ORCID 0000-0002-5087-9522
Wei WangSchool of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China.ORCID 0000-0002-3733-3939

Funding

College Students' Innovation and Entrepreneurship Training Program of Hubei Province S202410488120Entrepreneurship Fund for Graduate Students of Wuhan University of Science and Technology JCX2023049, JCX2023160Fund of Hubei Key Laboratory of Inland Shipping Technology and Innovation NHHY2023004Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System (Wuhan University of Science and Technology) ZNXX2023QNO3Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education SCCI2024YB02Nature Science Foundation of Hubei Province 2025AFB056Research Project of Hubei Provincial Department of Science and Technology 2024CSA075
6 · The paper itself

Abstract

This paper proposes a novel Denoiser and Low-Frequency Enhancer Network (DLFE-Net) for Low-Light Image Enhancement (LLIE). The DLFE-Net addresses two key challenges: (1) overexposure and detail loss in local areas during enhancement, and (2) the effective removal of inherent noise in low-light images. Specifically, the input RGB image is first converted to the HVI color space. The intensity (I) and color (H, V) maps are then enhanced and denoised separately, i.e., preserving details and removing noise. For preserving details, the Low-Frequency Illumination Enhancer (LFIE) module isolates and processes the image's low-frequency information. This targeted approach effectively mitigates local overexposure and preserves fine details during enhancement. For removing noise, the Multi-Scale Gated Denoiser (MSGD) module performs denoising through strong preservation after predicting image noise. Comprehensive experiments were conducted on three benchmark datasets (LOL, SICE, Sony-Total-Dark) and five unpaired datasets. Both qualitative and quantitative analyses demonstrated the superiority of DLFE-Net over state-of-the-art methods. Moreover, ablation studies demonstrated the effectiveness of each module in DLFE-Net.

Indexed as

color space conversionfrequency domain processinglow-frequency illumination enhancerlow-light image enhancementmulti-scale gated denoiser

Identifiers

PMID40942781
PMCPMC12431520

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.