Hologram Noise Model for Data Augmentation and Deep Learning
Dániel Terbe,
László Orzó,
Barbara Bicsák
et al.
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 c… Show more
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