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October 18, 2025Journal of Informatics and Web EngineeringOpen Access

Deep Learning Approaches to Autocorrelation Function and Signal-to-Noise Ratio Estimation in Noisy Images

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Authors

KLKai Liang LewKSKok Swee SimSTShing Chiang Tan

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Overview

This work demonstrates a novel CNN-based approach for accurate signal-to-noise ratio estimation in SEM images, suggesting improvements over classical methods.

Key Points

  • CalibNet achieved superior performance in estimating signal-to-noise ratio from SEM images.
  • Mean absolute error and root mean square error metrics indicate significant improvement over classical methods.
  • The proposed CNN-based architecture effectively addresses issues of Gaussian noise in imaging tasks.
  • Statistical analyses affirm that CalibNet's predictions align closely with established SNR values.

Cite This Study

Lew et al. (2025) studied this question.

synapsesocial.com/papers/68f408995de60f8893c6fe3ehttps://doi.org/10.33093/jiwe.2025.4.3.13
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