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Development of Schlieren optical imaging quality optimization and artifact removal technology based on deep neural network

Wen-Lin Chu, Jia-Ming Zhou, Yi-Wei Lin, and Bo-Lin Jian

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摘要 Abstract

本研究開發一套紋影影像品質提升之軟硬體整合最佳化方案，針對傳統紋影技術在影像清晰度與細節呈現上的根本性限制進行改善。我們建構了基於 Z 型光路配置的精密光學系統,並提出創新的雙階段影像處理策略，結合盲去卷積技術 (Blind Deconvolution) 與條件式生成對抗網路 (Conditional Generative Adversarial Networks，CGANs)，有效緩解單離軸紋影系統中的偽影問題。 This research developed an integrated hardware and software optimization solution for Schlieren image quality enhancement, addressing fundamental limitations in traditional Schlieren techniques regarding image clarity and detail presentation. We constructed a precision optical system based on Z-type optical path configuration and proposed an innovative two-stage image processing strategy that combines blind deconvolution techniques with Conditional Generative Adversarial Networks (CGANs) to effectively mitigate artifact issues in single off-axis Schlieren systems.

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關鍵字 Keywords

  • 紋影技術 Schlieren technique
  • 影像增強 Image enhancement
  • 深度學習應用 Deep learning applications
  • 條件生成對抗網絡 Conditional Generative Adversarial Networks (CGANs)
  • 盲去卷積 Blind deconvolution

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研究結果示例

單離軸紋影系統（General system）vs. Z 型紋影系統（Z-type system）

Schlieren System Overview

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引用格式 BibTeX

@InProceedings{ChuICMLSC2025,
  author    = {Jia-Ming Zhou and Wen-Lin Chu and Bo-Lin Jian},
  title     = {Implementation Analysis of Traditional and Z-Configuration Schlieren Systems in Machine Learning Applications},
  year      = {2025},
  month     = {10},
  address   = {Tokyo, Japan},
  url       = {https://icmlsc.org/2025.html},
  abstract  = {Schlieren imaging is a non-invasive flow visualization technique based on light propagation and projection principles. This study analyzes the differences between Z-type and standard Schlieren setup methods and discusses their applications in machine learning. The standard Schlieren system employs a simplified configuration comprising a light source and a concave mirror. The process begins by positioning the light source (such as a point source or laser) at the focal point of the concave mirror, generating parallel light beams after reflection. These parallel beams pass through the test subject, with a knife edge placed directly at the focal point to cut the light rays, thereby enhancing the visibility of light deflection. The Z-type configuration represents a typical Schlieren photography system arrangement that utilizes two concave mirrors in a Z-shaped alignment. This arrangement effectively extends the optical path while maintaining system compactness and symmetry. Consequently, this research compares these two setup methods and explores their implementation outcomes in machine learning applications. With the continuous advancement of machine learning technologies, this study anticipates that these techniques will find increasingly widespread applications in schlieren analysis, potentially enhancing the efficiency and accuracy of industrial automated inspection processes.},
}