Hologram Noise Model for Data Augmentation and Deep Learning
Author:
Terbe Dániel1ORCID, Orzó László1, Bicsák Barbara1, Zarándy Ákos1
Affiliation:
1. HUN-REN Institute for Computer Science and Control (SZTAKI), 1111 Budapest, Hungary
Abstract
This paper introduces a noise augmentation technique designed to enhance the robustness of state-of-the-art (SOTA) deep learning models against degraded image quality, a common challenge in long-term recording systems. Our method, demonstrated through the classification of digital holographic images, utilizes a novel approach to synthesize and apply random colored noise, addressing the typically encountered correlated noise patterns in such images. Empirical results show that our technique not only maintains classification accuracy in high-quality images but also significantly improves it when given noisy inputs without increasing the training time. This advancement demonstrates the potential of our approach for augmenting data for deep learning models to perform effectively in production under varied and suboptimal conditions.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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