Abstract

The use of electronic visual displays for background-oriented schlieren allows for the quick change of the reference images. In this study, we show that the quality of synthetic and background-oriented schlieren images can be improved by acquiring a set of images with different reference images and generating a median displacement field from it. To explore potential benefits, we studied different background changing strategies and their effect on the quality of the evaluation of the displacement field via artificial and experimental image distortions.Graphic abstract

Highlights

  • Background-oriented schlieren (BOS) or synthetic schlieren imaging is widely used to measure density gradients in transparent media

  • As proposed by Dalziel et al (2007), we present in this study the effectiveness of using multiple different reference images to reduce the systematic error via numerical calculations and the use of a backlit monitor as a background to realize these improvements in an experiment

  • This distortion of the reference images is used to evaluate the performance of the dynamic background scheme on a constant density gradient, which can be caused by optical lenses

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Summary

Introduction

Background-oriented schlieren (BOS) or synthetic schlieren imaging is widely used to measure density gradients in transparent media. As proposed by Dalziel et al (2007), we present in this study the effectiveness of using multiple different reference images to reduce the systematic error via numerical calculations and the use of a backlit monitor as a background to realize these improvements in an experiment. In both numerical study and experiment, the generation of the individual background patterns is based on 2D simplex noise and the displacement field is calculated via the optical flow algorithm by Farnebäck (2003), as implemented in the computer vision library OpenCV (Bradski 2000). The same background changing schemes are tested by measuring the displacement field introduce by placing a Fresnel lens between camera and background

Methods
Background generation
Artificial distortions applied to the background
Discrete single‐axis shift
Discrete double‐axis shift
Two‐axis linear shift
Optical flow
Evaluation of the accuracy of the calculated displacement field
Strategies for background changes
Position change
Pattern resolution
Seed change
Strategy combinations
Numerical evaluation of artificial distortions
Comparison between median and mean
Application to experiment
Measurement of artificial distortions
Measurement of Fresnel lens
Findings
Conclusion
Full Text
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