Image retrieval after propagation through multi-mode fibers is gaining attention due to their capacity to confine light and efficiently transport it over distances in a compact system. Here, we propose a generally applicable information-theoretic framework to transmit maximal-entropy (data) images and maximize the information transmission over sub-meter distances, a crucial capability that allows optical storage applications to scale and address different parts of storage media. To this end, we use millimeter-sized square optical waveguides to image a megapixel 8-bit spatial-light modulator. Data is thus represented as a 2D array of 8-bit values (symbols). Transmitting 100000s of symbols requires innovation beyond transmission matrix approaches. Deep neural networks have been recently utilized to retrieve images, but have been limited to small (thousands of symbols) and natural looking (low entropy) images. We maximize information transmission by combining a bandwidth-optimized homodyne detector with a differentiable hybrid neural-network consisting of a digital twin of the experiment setup and a U-Net. For the digital twin, we implement and compare a differentiable mode-based twin with a differentiable ray-based twin. Importantly, the latter can adapt to manufacturing-related setup imperfections during training which we show to be crucial. Our pipeline is trained end-to-end to recover digital input images while maximizing the achievable information page size based on a differentiable mutual-information estimator. We demonstrate retrieval of 66 kB at maximum with 1.7 bit per symbol on average with a range of 0.3 - 3.4 bit.
翻译:在通过多模式纤维进行传播后,图像检索日益受到关注,这是因为它们有能力限制光亮,并在紧凑系统中将光有效传输到远处。在这里,我们提出一个普遍适用的信息理论框架,以传输最大-耐光(数据)图像,并在亚米距离内最大限度地传播信息,这是一个关键能力,使光存储应用程序能够缩放和处理存储介质的不同部分。为此,我们使用毫米尺寸的平方光波导师来图像巨型像像8比特空间光度调色仪。因此,数据代表着8比值的2D阵列(符号)。传输100万个符号需要创新,而不是传输矩阵方法。最近,深神经网络被用于检索图像,但仅限于小(千个符号)和自然(低温)图像。我们通过将带宽频频的同声调同声波检测器与不同的混合神经网络结合,由实验的数码双链接和U-Net组成。对于数字双对数字双位的双位图像,我们实施并比较了双级的双向关键图像,然后在以我们所训练的可移动的图像中进行双向式同步的模拟的图像,然后显示一个基于可移动的图像的图像。