Evidence map›Paper›PMID 39275687›Full record

ArticleSensors (Basel, Switzerland)2024

Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels.

Yi Wang, Zhihua Chen, Guoxu Yan, Jiarui Zhang, Bo Hu

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. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Yi WangNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0009-0009-0634-3622
Zhihua ChenNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Guoxu YanNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Jiarui ZhangNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.ORCID 0009-0005-0105-2878
Bo HuNational Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Underwater image enhancement technology is crucial for the human exploration and exploitation of marine resources. The visibility of underwater images is affected by visible light attenuation. This paper proposes an image reconstruction method based on the decomposition-fusion of multi-channel luminance data to enhance the visibility of underwater images. The proposed method is a single-image approach to cope with the condition that underwater paired images are difficult to obtain. The original image is first divided into its three RGB channels. To reduce artifacts and inconsistencies in the fused images, a multi-resolution fusion process based on the Laplace-Gaussian pyramid guided by a weight map is employed. Image saliency analysis and mask sharpening methods are also introduced to color-correct the fused images. The results indicate that the method presented in this paper effectively enhances the visibility of dark regions in the original image and globally improves its color, contrast, and sharpness compared to current state-of-the-art methods. Our method can enhance underwater images in engineering practice, laying the foundation for in-depth research on underwater images.

Indexed as

luminance reconstructionmulti-resolution fusionoriginal image fusionunderwater images

Identifiers

PMID39275687
PMCPMC11397948

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.