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            <title xml:lang="en">Using Deep Learning for Object Distance Prediction in Digital Holography</title>
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              <p>Deep Learning (DL) has marked the beginning of a new era in      computer science, particularly in Machine Learning (ML). Nowadays,      there are many fields where DL is applied such as speech      recognition, automatic navigation systems, image processing,      etc&amp;nbsp;[1]. In this paper, a Convolutional Neural Network (CNN), more      precisely a CNN built on top of DenseNet169, is proven to be      helpful in predicting object distance in computer-generated      holographic images. The problem is addressed as a classification      problem where 101&amp;nbsp;classes of images were generated, each class      corresponding to a different distance value from the object at a      micrometer scale. Experiments show that the proposed network is      efficient in this context, being able to classify with a 100%      accuracy level if trained properly.</p>
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