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Related Experiment Video

Updated: Jul 3, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Hologram Noise Model for Data Augmentation and Deep Learning.

Dániel Terbe¹, László Orzó¹, Barbara Bicsák¹

¹HUN-REN Institute for Computer Science and Control (SZTAKI), 1111 Budapest, Hungary.

Sensors (Basel, Switzerland)

|February 10, 2024

Summary

This study introduces a noise augmentation technique to improve deep learning model performance on low-quality images. The method enhances classification accuracy for noisy digital holographic images without extra training time.

Keywords:

CNN deep learning hologram image augmentation image processing neural networks noise modeling

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Related Experiment Videos

Last Updated: Jul 3, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Evaluation and Manipulation of Neural Activity Using Two-Photon Holographic Microscopy

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Published on: September 16, 2022

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Published on: January 14, 2020

Area of Science:

Computer Vision
Machine Learning
Image Processing

Background:

Deep learning models struggle with degraded image quality in long-term recordings.
Correlated noise patterns are common in digital holographic images.
Robustness against image degradation is crucial for real-world applications.

Purpose of the Study:

To develop a noise augmentation technique for enhancing deep learning model robustness.
To improve classification accuracy on degraded digital holographic images.
To address the challenge of correlated noise in image data.

Main Methods:

A novel method for synthesizing and applying random colored noise was developed.
The technique was applied to digital holographic image classification tasks.
The approach focused on augmenting training data to simulate real-world noise.

Main Results:

Classification accuracy was maintained on high-quality images.
Significant improvements in accuracy were observed on noisy input images.
The noise augmentation technique did not increase model training time.

Conclusions:

The proposed noise augmentation technique effectively enhances deep learning model robustness.
This method offers a viable solution for improving performance in suboptimal imaging conditions.
The approach has potential for broader application in data augmentation for deep learning.