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Corrigendum: Identifying single electron charge sensor events using wavelet edge detection
J R Prance1, B J Van Bael2, C B Simmons2
1Department of Physics, Lancaster University, Lancaster, LA1 4YB, United Kingdom.
Nanotechnology
|December 28, 2021
Summary
Wavelet edge detection is superior to thresholding for image analysis with white and 1/f noise. New results confirm this finding after correcting simulated noise descriptions.
Area of Science:
- Signal Processing
- Image Analysis
- Computational Imaging
Context:
- Benchmarking edge detection algorithms requires accurate noise simulation.
- Previous work incorrectly described the simulated noise.
- This study corrects the noise description and re-evaluates algorithm performance.
Purpose:
- To correct the description of simulated noise used in benchmarking wavelet edge detection.
- To provide new results based on accurately simulated noise.
- To reaffirm the superiority of wavelet edge detection over thresholding.
Summary:
- The simulated noise for benchmarking wavelet edge detection was corrected.
- New results confirm that wavelet edge detection outperforms thresholding.
- This holds true for both white noise and 1/f noise conditions.
Impact:
- Provides accurate methodology for future research in image noise analysis.
- Confirms the robustness and effectiveness of wavelet edge detection.
- Offers improved image processing techniques for noisy datasets.
