remove the image suggestion and extra linespaces from this sentence:Slide 1: Setting the Stage—Why SSD Matters in Optics  • Subsurface damage (SSD) reduces optical performance and component lifespan  • High-precision optics require reliable, objective SSD analysis  • Industry and research demand scalable, reproducible solutions  Slide 2: Current SSD Analysis—Manual, Slow, and Subjective  • Manual inspection is time-consuming and operator-dependent  • Inconsistent results across labs and users  • Growing data volumes outpace traditional methods  Slide 3: Project Context—MoKI-Pro and Digitalization in Optics  • MoKI-Pro project aims to digitize and automate optics manufacturing  • Collaboration between academia and industry for process innovation  • Your thesis contributes to the digital transformation of SSD analysis  COMPLICATION  Slide 4: The Challenge—Artifacts and Inconsistent Boundaries  • Scratches and periodic noise obscure true SSD features in images  • Calotte boundaries often faint, noisy, or incomplete  • Manual measurement struggles with low-contrast or damaged samples  Slide 5: Limitations of Existing Algorithms  • Most existing methods require fixed parameters and manual tuning  • Poor adaptability to different materials and grinding conditions  • High error rates in images with severe surface damage  Slide 6: Need for Automation and Objectivity  • Demand for high-throughput, unbiased SSD analysis  • Reproducibility and traceability are essential for research and industry  • Opportunity to leverage digital image processing for better results  QUESTION  Slide 7: Key Question—How Can We Achieve Reliable, Automated SSD Analysis?  • How to objectively detect calotte boundaries in diverse, noisy images?  • How to suppress artifacts without losing true SSD features?  • How to ensure the process is reproducible and scalable?  ANSWER  Slide 8: Solution Overview—Automated Image Analysis Pipeline  • Modular workflow: preprocessing, circle detection, FFT artifact suppression  • Designed for flexibility and reproducibility  • Validated on N-BK7, Zerodur, and Fused Silica samples  Slide 9: Preprocessing—Laying the Foundation for Reliable Analysis  • Grayscale conversion, noise reduction, and contrast enhancement  • Morphological operations to clean up artifacts and smooth edges  • Ensures calotte boundaries are distinct and ready for detection  Image suggestion: Before/after preprocessing comparison  Slide 10: Image Acquisition—Capturing High-Quality Data  • Keyence VHX-600 microscope with multiple objectives for medium and fine grinding  • Careful selection of magnification and lighting (ring, coaxial, multi-lighting)  • Consistent sample handling to avoid new artifacts  Image suggestion: Photo of microscope setup or sample image  Slide 11: Image Acquisition—Optimizing Lighting and Focus  • Use of ring light for surface topography, coaxial for fine cracks  • Multi-lighting and HDR functions for challenging samples  • Auto-focus and anti-vibration features for sharp, clear images  Slide 12: Image Acquisition—Ensuring Reproducibility  • Standardized imaging protocols and metadata documentation  • Calibration and lens recognition for accurate scaling  • Consistent file naming and storage for traceability  Slide 13: Automatic Calotte Detection—Mathematical Objectivity  • Edge detection and contour extraction using OpenCV  • Circularity metric selects the most plausible calotte boundary  • Least-squares circle fitting for precise diameter measurement  Image suggestion: Workflow diagram or overlay of detected circle on sample image  Slide 14: Automatic Calotte Detection—Performance Across Samples  • High accuracy in fine/medium-ground images (deviation <2% from manual)  • Robust to moderate noise and partial occlusion  • Limitations in severely damaged or non-circular calottes  Slide 15: FFT Artifact Suppression—Removing Periodic Noise  • FFT transforms image to frequency domain, revealing periodic artifacts  • Detected peaks are suppressed, cleaning up scratches and striations  • Inverse FFT reconstructs a clearer image for SSD analysis  Image suggestion: 6-panel figure showing original, FFT, detected peaks, and artifact-suppressed result (e.g., Z_S2-M.png)  Slide 16: FFT Artifact Suppression—Results and Limitations  • Effective in samples with moderate scratching (e.g., Z_S2-M, F_S8-M, S10-R)  • Limited effectiveness in heavily scratched or damaged samples  • Highlights need for careful sample prep and adaptive algorithms  Slide 17: Pipeline Performance—Efficiency and Reproducibility  • Automated analysis reduces processing time from minutes to seconds per image  • Consistent results across materials and grinding conditions  • Objectivity eliminates operator bias and supports high-throughput analysis  Slide 18: Lessons Learned—What Worked and What Needs Improvement  • Preprocessing and circle detection are robust for most cases  • FFT suppression is powerful but not a cure-all  • Sample quality and process control remain critical for best results  Slide 19: Future Work—Toward Full Automation and Smarter Analysis  • Integrate metadata extraction for magnification and lighting  • Explore adaptive filtering and machine learning for artifact suppression  • Develop automated SSD metric extraction and 3D analysis  Slide 20: Conclusion—Raising the Bar for SSD Analysis in Optics  • Automated pipeline sets a new standard for objectivity and efficiency  • Supports smarter, data-driven manufacturing and research  • Lays the groundwork for future innovation in SSD analysis