Fig 1.
Architecture of the image denoising process showing the noise-free output.
Table 1.
Summary of studies on Image De-noising using Deep Learning techniques.
Fig 2.
Architecture of the proposed methodology illustrating the systematic workflow for image denoising.
Table 2.
Hyperparameters setting of the proposed convolutional neural network alorithm during training for noise detection.
Fig 3.
De-noising autoencoder Architecture for image restoration from input noisy images to clean images.
Fig 4.
De-noising performance result of auto-encoder with fuzzy median filter on real-world noisy images with different noise level.
Table 3.
Noise detection performance metrics at various noise densities.
Fig 5.
Performance Comparison of Accuracy and F-Score Across Different Noise Densities.
Fig 6.
Assessment of False Positive and False Negative Rates.
Table 4.
Comparison of the proposed method with state-of-the-art image de-noising techniques.
Fig 7.
Various noise level densities for image de-noising.