Evidence map›Paper›PMID 42740283›Full record

ArticleSensors (Basel, Switzerland)2026

Underwater Image Enhancement via Multiple-Enhanced-Layers Fusion and Transmission-Driven Color Restoration.

Zhengmao Li, Chi Zhang, Yanping Chen, Jun Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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
–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

0 citing papers in PubMed.

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.

Zhengmao LiSchool of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Chi ZhangSchool of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.ORCID 0009-0007-1900-094X
Yanping ChenSchool of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Jun ZhangSchool of Artificial Intelligence, Anhui University, Hefei 230601, China.ORCID 0000-0002-5985-8023

Funding

Key Project of Teaching and Research at Hefei University 2023 hfujyd05Research on Privacy Data Security Sharing Technology Based on Blockchain and CP-ABE 2024 AH051527
6 · The paper itself

Abstract

Underwater images captured by sensors suffer from low contrast and blurry details due to the interference of light absorption and scattering in underwater scenes. Good visibility restoration is often desired for practical processing applications. Current image enhancement methods often rely on prior assumptions to reconstruct a clear image without considering the inherent correlation of underwater image degradation, introducing unconsiderable enhancement results. Thus, this paper proposes an underwater enhancement method based on multiple enhanced layers fusion and transmission-driven color restoration, named EFCR, which consists of three key modules: a pixel-based transmission computation (PTC), a multiple-enhanced-layers fusion (MELF), and a transmission-driven color restoration (TCR). First, PTC designs a linear transformation to adjust the saturation and estimates the transmission based on the mapping relationship between the transmission, the brightness, and the saturation, preventing the transmission from being under-estimated. Then, MELF extracts the original details from the luminance channel and enhances these desired details based on the estimated transmission. Meanwhile, adaptive histogram equalization is used to improve the global brightness. Finally, TCR further analyzes the inherent correlation between the transmission and the image degradation, and constructs a compensation factor to adaptively correct the attenuated

Indexed as

image restorationpixel-based transmission estimationtransmission-driven color restorationunderwater image enhancement

Identifiers

PMID42740283
PMCPMC13568284

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.