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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...

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Related Experiment Video

Updated: Aug 24, 2025

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

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Experimental Study of Radial Distortion Compensation for Camera Submerged Underwater Using Open SaltWaterDistortion

Daria Senshina^1,2, Dmitry Polevoy^3,4,5, Egor Ershov⁶

¹Evocargo LLC, 129085 Moscow, Russia.

Journal of Imaging

|October 26, 2022

Summary

This summary is machine-generated.

A new open dataset of underwater chessboard images aids in evaluating radial distortion correction methods. The study confirms a formula effectively corrects underwater camera distortion with high precision.

Keywords:

camera calibration radial distortion refractive index underwater shooting

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Area of Science:

Computer Vision
Image Processing
Optics

Background:

Underwater imaging presents challenges due to light refraction.
Radial distortion is a common issue in underwater photography.
Accurate correction is crucial for various applications.

Purpose of the Study:

Introduce a novel open dataset for underwater image analysis.
Evaluate the effectiveness of a radial distortion correction formula for submerged cameras.
Assess the robustness of the correction method against variations in refractive index.

Main Methods:

Collected 662 underwater chessboard images with varying salinity (refractive index).
Utilized telephoto and wide-angle mobile phone cameras.
Manually annotated 35,748 chessboard nodes across all images.
Applied and validated a radial distortion correction formula.

Main Results:

The radial distortion correction formula achieves high precision, comparable to classical calibration.
The correction method demonstrates resilience to minor inaccuracies in refractive index measurements.
The open dataset and code facilitate further research in underwater image correction.

Conclusions:

The developed dataset and correction formula offer a valuable tool for underwater imaging research.
The proposed correction method is practical and reliable for real-world underwater applications.
Public availability of data and code promotes advancements in the field.