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Related Experiment Video

Updated: Sep 22, 2025

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Reefshape: A System for the Efficient Collection and Automated Processing of Time-Series Underwater Photogrammetry Data for Benthic Habitat Monitoring

Published on: June 13, 2025

A Underwater Sequence Image Dataset for Sharpness and Color Analysis.

Miao Yang¹, Ge Yin¹, Haiwen Wang¹

¹School of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, China.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

A new dataset, TankImage-I, addresses underwater image quality issues like blur and color shifts. It aids in developing better underwater imaging systems and algorithms for assessing image quality.

Keywords:

sequence image underwater dataset underwater image quality

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Area of Science:

Computer Vision
Image Processing
Oceanography

Background:

Underwater environments degrade image quality, primarily affecting sharpness and color.
Existing datasets may not fully capture the dynamic challenges of underwater imaging.

Purpose of the Study:

Introduce TankImage-I, a novel underwater image dataset.
Evaluate the performance of existing image quality assessment (IQA) methods under varied underwater conditions.
Provide a benchmark for developing improved IQA algorithms and underwater imaging systems.

Main Methods:

Collected a sequence of 78 underwater images (TankImage-I) in a pool with two plane targets.
Varied lighting conditions, water transparency, and imaging distances during data acquisition.
Measured sharpness and color distortion for the collected image sequences.
Tested and analyzed 14 different image quality assessment methods using the TankImage-I dataset.

Main Results:

TankImage-I exhibits gradually changing sharpness and color distortion.
Performance analysis of 14 IQA methods revealed their effectiveness and limitations under specific underwater challenges.
Quantitative measurements of sharpness and color distortion were provided for the dataset.

Conclusions:

TankImage-I serves as a valuable resource for advancing underwater image quality assessment.
The study provides insights into the performance of current IQA algorithms, guiding future research and development.
Findings support the design of more robust underwater imaging systems and algorithms.