Iris Optics
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Optical MetrologyMachine VisionData Pipelines

Rainbow Stray Light Analysis Framework

Developing image-processing and perceptual metrics for stray light characterization.

Confidentiality & Anonymization Note

These examples are generalized and anonymized to illustrate Iris Optics capabilities. Details are modified to protect confidentiality and avoid disclosure of proprietary employer, client, or product information.

Challenge

Quantifying visually meaningful stray light artifacts (often referred to as 'rainbows') in complex optical systems can be highly subjective and manual.

Approach

We developed an analysis framework using advanced image segmentation, morphology, and region metrics. We applied perceptual weighting to the data to generate repeatable output reports.

Outcome

This methodology demonstrates how engineering teams can achieve faster analysis, improved comparability between different designs, and better, data-driven decisions.

Background

Stray light artifacts, particularly colorful “rainbow” ghosting effects, can severely degrade the user experience in near-eye displays and advanced imaging systems. However, “bad” stray light is notoriously difficult to quantify.

The Challenge

Engineering teams often rely on subjective visual inspection to evaluate stray light. One engineer might score a lens a “fail” while another scores it a “pass”. The lack of objective, repeatable metrics makes it difficult to track design improvements or set clear manufacturing tolerances.

The Approach

We developed a custom image-processing workflow to turn subjective perception into objective data:

  1. Image Segmentation: We implemented algorithms to isolate specific stray light artifacts from the background image data.
  2. Morphological Analysis: We analyzed the shape, size, and intensity distribution of the isolated artifacts.
  3. Perceptual Weighting: Crucially, we applied weighting functions that penalize artifacts based on their location in the field of view and their color contrast against the background, mimicking human visual perception.
  4. Automated Reporting: We built tools to automatically generate standardized reports for every tested lens.

The Outcome

This framework replaces subjective arguments with objective, repeatable data. Engineering teams can directly compare the stray light performance of different optical designs, and quality assurance processes can establish clear, data-driven pass/fail criteria.