Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

Image reconstruction through a multimode fiber with a simple neural network architecture

“…By design, CNNs excel at processing local features and do not perform as well in processing global features 46 . It would be interesting to explore modifications to the CNN scheme, or preprocessing schemes for the speckle pattern, to improve performance 22 .…”

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

“…The DNN is trained in batches of 100 images for a maximum of 50 epochs. The collected images of the dataset are split so that 80% are used for training, The dataset used to assess the performance of the DNN is generated by projecting phase images of handwritten digits at the proximal fiber side [16][17][18]20]. These images are available online in the MNIST database, which is widely used to test the capabilities of different neural network architectures [28].…”

“…While phase conjugation and transmission matrix can control the light propagation through an MMF probe, they are based on interferometric measurements of the light field using digital holography, and as a result they are susceptible to environmental or experimental perturbations, requiring dynamic recalibration [8,[13][14][15]. To overcome the sensitivity of the calibration-based measurements, deep neural networks (DNNs) have been proposed as an alternative for imaging through MMFs [16][17][18][19][20]. The idea of using artificial neural networks (ANNs) to interpret the information after propagation in a MMF was first reported by Aisawa et al [21,22] in 1991 using a simple neural network architecture.…”

“…Recently, the advancements in computational means allow the realization of more complex neural network architectures that can handle data of high-dimensionality, bringing DNNs to the forefront of many leading technologies ranging from research and business to military and entertainment [23][24][25]. DNNs have shown remarkable capabilities in recovering information through MMFs for imaging applications, while preserving robustness against perturbations in the system [16][17][18]20,26].…”

Intensity-Only Mode Decomposition on Multimode Fibers Using a Densely Connected Convolutional Network

“…A CNN is applied for the MD of an MMF, which is the subject of the work presented. It has already been shown that complex problems using purely intensity-based signal evaluation employing neural networks provide a yield comparable to that of a holographic counterpart [25], [26]. For the present problem, the goal is to transmit a pure intensity image into the CNN, which predicts the complex mode weights.…”