Evidence map›Paper›PMID 35591240›Full record

ArticleSensors (Basel, Switzerland)2022

A Underwater Sequence Image Dataset for Sharpness and Color Analysis.

Miao Yang, Ge Yin, Haiwen Wang, Jinnai Dong, Zhuoran Xie, Bing Zheng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

6 authors.

Miao YangSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Ge YinSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Haiwen WangSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Jinnai DongSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Zhuoran XieSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.
Bing ZhengDepartment of Information Science and Engineering, Ocean University of China, Qingdao 266001, China.

Funding

Graduate Research and Practice Innovation Program DZXS202004; DZXS202003; KYCX2021-053Jiangsu Basic Research Program BK20191469Jiangsu Natural Resources Development Special Fund JSZRhykj202116National Natural Science Foundation of China 12171205
6 · The paper itself

Abstract

The complex underwater environment usually leads to the problem of quality degradation in underwater images, and the distortion of sharpness and color are the main factors to the quality of underwater images. The paper discloses an underwater sequence image dataset called TankImage-I with gradually changing sharpness and color distortion collected in a pool. TankImage-I contains two plane targets, a total of 78 images. It includes two lighting conditions and three different water transparency. The imaging distance is also changed during the photographing process. The paper introduces the relevant details of the photographing process, and provides the measurement results of the sharpness and color distortion of the sequence images. In addition, we verify the performance of 14 image quality assessment methods on TankImage-I, and analyze the results of 14 image quality assessment methods from the aspects of sharpness and color, which provides a reference for the design and improvement of underwater image quality assessment algorithm and underwater imaging system design.

Indexed as

AlgorithmsLightingWaterWatersequence imageunderwater datasetunderwater image quality

Identifiers

PMID35591240
PMCPMC9100472

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.