Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography

Stéphane Cuenat; Raphaël Couturier

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arXiv · 2108.09147

Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography

Stéphane Cuenat·Raphaël Couturier

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Abstract

In Digital Holography (DH), it is crucial to extract the object distance from a hologram in order to reconstruct its amplitude and phase. This step is called auto-focusing and it is conventionally solved by first reconstructing a stack of images and then by sharpening each reconstructed image using a focus metric such as entropy or variance. The distance corresponding to the sharpest image is considered the focal position. This approach, while effective, is computationally demanding and time-consuming. In this paper, the determination of the distance is performed by Deep Learning (DL). Two deep learning (DL) architectures are compared: Convolutional Neural Network (CNN) and Vision Transformer (ViT). ViT and CNN are used to cope with the problem of auto-focusing as a classification problem. Compared to a first attempt [11] in which the distance between two consecutive classes was 100$μ$m, our proposal allows us to drastically reduce this distance to 1$μ$m. Moreover, ViT reaches similar accuracy and is more robust than CNN.

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BibTeX RIS

Stéphane Cuenat, Raphaël Couturier. 2022-01-27. Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography. https://arxiv.org/abs/2108.09147

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