A robust deep learning approach for impulse noise filtering using hybrid auto-encoder with fuzzy median filter

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not quantified - described as 'significant improvement'

• Primary Outcome: Image quality improvement measured by PSNR and SSIM

• Nnt Or Sensitivity: Classification metrics mentioned (accuracy, FPR, FNR, F1-score) but values not provided in abstract

• Confidence Interval: Not reported

Clinical Application

Unclear - computational requirements and implementation complexity not specified Potential relevance for medical imaging in radiology departments, but would require TGA validation for clinical use and integration with existing PACS systems Limited applicability - primarily relevant for medical imaging applications requiring noise reduction

Abstract

De-noising convolutional neural networks (DnCNNs), are a powerful nonlinear mapping models in image processing for impulse noise removal. During training and validation, a set of 12 standard testing images is used to evaluate model performance. DnCNNs demonstrate strong capability in classification of impulse noise with excellent results. To evaluate de-noising performance, a suitable noise ratio should be added so that most appropriate DnCNN model can be used for impulse noise detection. This research proposes an effective image restoration technique that integrates DnCNN and an autoencoder with a fuzzy median filter to detect and eliminate high-density impulse noise. The proposed deep learning de-noising technique used to classify noisy and clean pixels, and result are presented in different metrics such as accuracy, FPR, FNR and f1 score. Further, to remove impulse noise an auto-encoder with fuzzy median filter are used that then reconstructs the clean image based with parametric values. Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), are used to assess our methodology, it is compared to conventional impulse noise filtering techniques, experimental results indicate a significant improvement in image quality. Based on the final de-noised images, this research contributes to developing deep learning-based, de-noising techniques that enhance image restoration quality while preserving image details and essential features.

References

1. 1.Naeem, M., Bhatti, S. M., Rashid, M., Jaffar, A., Akram, S., Fida, B., & Ahmad, A. (2024). A robust deep learning approach for impulse noise filtering using hybrid auto-encoder with fuzzy median filter. PLoS One. https://doi.org/10.1371/journal.pone.0343141