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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

A Review of Neural Network-Based Image Noise Processing Methods.

Anton A Volkov¹, Alexander V Kozlov¹, Pavel A Cheremkhin¹

¹Laser Physics Department, Institute for Laser and Plasma Technologies, National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe Shosse 31, 115409 Moscow, Russia.

Sensors (Basel, Switzerland)

|October 16, 2025

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Neural networks offer advanced solutions for digital image noise processing, outperforming traditional methods in complex scenarios. These deep learning techniques enhance image quality in critical fields like forensics and medical diagnostics.

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Digital cameras are essential in forensics and medical diagnostics, but image noise degrades quality.
Traditional noise suppression methods require extensive parameter tuning and struggle with complex data.
Neural networks present a promising alternative for effective digital image noise processing.

Purpose of the Study:

To review neural network-based methods for digital image noise processing.
To discuss the application of neural networks in noise estimation, suppression, and analysis.
To highlight challenges and future directions in neural network-based image noise reduction.

Main Methods:

Convolutional Neural Networks (CNNs)
Autoencoders

Keywords:

camera noise camera source identification convolutional neural networks deep learning denoising generative adversarial networks image noise noise estimation photo-response non-uniformity synthetic image

Related Experiment Videos

Last Updated: Jan 15, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Generative Adversarial Networks (GANs)

Main Results:

Neural networks effectively handle complex noise patterns and adapt to varying conditions.
Applications include noise estimation, suppression, classification, and image source identification.
Extraction of unique camera fingerprints via photo response non-uniformity is explored.

Conclusions:

Neural networks significantly advance digital image noise processing capabilities.
Challenges remain in creating reliable training datasets and distinguishing image noise from photosensor noise.
Further research is needed to overcome these fundamental issues for improved image quality.