October 3, 2025SensorsOpen Access

A Review of Neural Network-Based Image Noise Processing Methods

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Neural networks can significantly improve noise estimation and suppression in digital images, enhancing overall quality.
Convolutional neural networks, autoencoders, and GANs are effective for handling complex noise patterns in various applications.
The challenges of generating reliable training datasets affect the performance of neural network methods for image noise processing.
Neural network applications extend to image classification and camera source identification, reinforcing the importance of image noise management.

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This review analyzes neural network methods for image noise suppression, indicating their effectiveness and challenges.

Volkov et al. (2025) studied this question.

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