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Quantum-implementable selective reconstruction of high-resolution images

机译:量子可执行的高分辨率图像选择性重建

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This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order to enable its quantumwave implementation based on holography. The main limitations of the classical Hopfield net are much reduced with the simulated new quantum-optical implementation.
机译:本文是为跨学科的读者而写的,提出了可通过量子光学实现的计算图像重建。已成功模拟了许多存储的图像中由输入触发的高分辨率图像的选择,以及其重构(如果输入被遮挡或嘈杂的情况)。对原始算法(基于Hopfield关联神经网络)进行了转换,以使其能够基于全息术实现量子波。模拟的新量子光学实现大大减少了经典Hopfield网络的主要局限性。

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