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Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness

Amit Pandey1, Mohd Zubair Akhtar2, Nandana Kappuva Veettil2

  • 1University of Applied Sciences, Institute of Innovative Mobility (IIMo), Research group Sensor Technology and Data Fusion for Environmental Perception, Esplanade 10, Ingolstadt, 85049, Germany. amit.pandey@thi.de.

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Summary
This summary is machine-generated.

This study introduces a new method to estimate automotive camera blurring kernels using Principal Component Analysis (PCA) and differential evolution optimization. This technique enables effective state monitoring of camera sharpness degradation.

Keywords:
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Area of Science:

  • Optical engineering
  • Image processing
  • Automotive technology

Background:

  • Camera sharpness is crucial for automotive applications, assessed via spatial frequency response (SFR) during end-of-line (EOL) testing.
  • Estimating the blurring kernel is a key step towards real-time state monitoring of automotive cameras.

Purpose of the Study:

  • To develop and validate a method for estimating the blurring kernel of automotive cameras.
  • To enable in-field monitoring of camera sharpness degradation.

Main Methods:

  • Utilized Principal Component Analysis (PCA) on synthetic kernels generated by Zemax, building a model with ~1300 spatially variant point spread functions (PSFs).
  • Developed an algorithm using synthetic images (convolved kernels with traffic signs) for training and real-world data for validation.
  • Employed differential evolution optimization to minimize the SFR difference between blurred reference ROIs and kernels, identifying the best-matching kernel.

Main Results:

  • Achieved high accuracy in kernel estimation, with Structural Similarity Index Measure (SSIM) between true and estimated kernels ranging from 0.92 to 0.98.
  • Validation on real-world automotive camera images showed SSIM > 0.82 for estimated vs. blurred ROIs.
  • Demonstrated promising performance with Pearson correlation coefficients (0.84-0.99) and Cosine similarity (0.86-0.99).

Conclusions:

  • The proposed method accurately estimates automotive camera blurring kernels.
  • This kernel estimation technique is a viable first step for in-field state monitoring of automotive cameras.
  • The approach shows potential for tracking sharpness degradation over time.