Summary of Implemented Methods and Techniques

In the course of this thesis, I have developed and validated several advanced image processing techniques to enhance the evaluation of subsurface damage (SSD) in optical materials, closely following the methodological recommendations outlined in Prof. Dr. Gerald Fütterer’s project guidelines. Two key innovations—automatic circle detection and automated peak detection in the FFT spectrum—have been successfully implemented and tested on challenging experimental data.

1. Automatic Circle Detection for Fine Grinding Images

For the analysis of fine grinding images, where the calotte boundary often becomes blurred due to the nature of the process, I developed a robust automatic circle detection algorithm. This method leverages the OpenCV library for image preprocessing, contour detection, and shape analysis. The workflow is as follows:
 • Preprocessing and Noise Reduction: The input image is first subjected to global thresholding to suppress background artifacts, followed by morphological operations (opening and closing) to further reduce noise and enhance the visibility of relevant features.
 • Contour Detection and Analysis: External contours are detected, and the algorithm iterates through them to identify the largest contour by area. Circularity is calculated for each contour to ensure that only those closely approximating a circle are considered.
 • Robustness to Blurring: Even in cases where the calotte edge is not sharply defined—such as in fine grinding images—the method reliably detects the calotte by focusing on geometric properties rather than relying solely on edge sharpness.
 • Visualization and Validation: The detected circle is drawn on the image, and the results are visualized using Matplotlib, allowing for immediate qualitative assessment of detection accuracy.

This approach has proven effective even under suboptimal imaging conditions, ensuring that the calotte diameter can be measured objectively and reproducibly, which is critical for subsequent SSD depth calculations.

2. Automated Peak Detection and Suppression in FFT Spectrum

A major challenge in SSD analysis is the presence of periodic scratches introduced during the Kalottentest, particularly by the steel ball. These scratches manifest as high-frequency components in the FFT spectrum of the microscope images, often obscuring the underlying SSD features. To address this, I implemented an automated peak detection and suppression technique in the frequency domain:
 • FFT Computation and Spectrum Analysis: The 2D Fast Fourier Transform (FFT) of the grayscale image is computed, and the magnitude spectrum is analyzed. A Gaussian filter is applied to reduce noise and enhance the clarity of significant frequency peaks.
 • Automatic Peak Detection: The algorithm identifies prominent peaks in the FFT spectrum, which correspond to the periodic scratches. These peaks are detected automatically, without manual intervention, ensuring consistency and objectivity.
 • Frequency Suppression: Once identified, the values at these peak frequencies are set to zero, effectively removing the corresponding periodic artifacts from the image.
 • Resulting Image Quality: The inverse FFT is then computed to reconstruct the filtered image. The removal of scratch artifacts reveals the true SSD features beneath, enabling more accurate and reliable evaluation.

This method is particularly valuable for images where scratches would otherwise mask subtle SSD patterns, and it aligns with the professor’s suggestion to use frequency-domain filtering for artifact suppression.

Alignment with Project Goals and Future Directions

Both techniques—automatic circle detection and FFT-based scratch removal—directly support the project’s aim of developing a semi-automated, reproducible pipeline for SSD evaluation. They reduce operator bias, improve measurement accuracy, and enable the analysis of challenging datasets that would be difficult to interpret manually.

Looking forward, these methods provide a strong foundation for further automation, such as integrating concentric ring segmentation for depth-resolved SSD analysis or applying machine learning for feature classification. The current results demonstrate that even in the presence of image blurring or strong periodic artifacts, advanced computational techniques can extract meaningful, quantitative information for process optimization in optical manufacturing.

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