Multimode fibers(MMFs)are an example of a highly scattering medium,which scramble the coherent light propagating within them to produce seemingly random patterns.Thus,for applications such as imaging and image project...Multimode fibers(MMFs)are an example of a highly scattering medium,which scramble the coherent light propagating within them to produce seemingly random patterns.Thus,for applications such as imaging and image projection through an MMF,careful measurements of the relationship between the inputs and outputs of the fiber are required.We show,as a proof of concept,that a deep neural network can learn the input-output relationship in a 0.75 m long MMF.Specifically,we demonstrate that a deep convolutional neural network(CNN)can learn the nonlinear relationships between the amplitude of the speckle pattern(phase information lost)obtained at the output of the fiber and the phase or the amplitude at the input of the fiber.Effectively,the network performs a nonlinear inversion task.We obtained image fidelities(correlations)as high as~98%for reconstruction and~94%for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix.We further show that the network can be trained for transfer learning,i.e.,it can transmit images through the MMF,which belongs to another class not used for training/testing.展开更多
We introduce a lock-in method to increase the phase contrast in incoherent differential phase contrast(DPC)imaging.This method improves the phase sensitivity by the analog removal of the background.The use of a smart ...We introduce a lock-in method to increase the phase contrast in incoherent differential phase contrast(DPC)imaging.This method improves the phase sensitivity by the analog removal of the background.The use of a smart pixel detector with in-pixel signal demodulation,paired with synchronized switching illumination,provides the basis of a bit-efficient approach to emulate a lock-in DPC.The experiments show an increased sensitivity by a factor of up to 8,as expected from theory,and a reduction of collected data by a factor of 70,for equivalent standard DPC measurements;single-shot sensitivity of 0.7 mrad at a frame rate of 1400 frames per second is demonstrated.This new approach may open the way for the use of incoherent phase microscopy in biological applications where extreme phase sensitivity and millisecond response time are required.展开更多
基金funding from the Swiss National Science Foundation(SNSF)project MuxWave(200021_160113/1)funded by CERAMIC X.0—High-precision micromanufacturing of ceramics(ID:14032).
文摘Multimode fibers(MMFs)are an example of a highly scattering medium,which scramble the coherent light propagating within them to produce seemingly random patterns.Thus,for applications such as imaging and image projection through an MMF,careful measurements of the relationship between the inputs and outputs of the fiber are required.We show,as a proof of concept,that a deep neural network can learn the input-output relationship in a 0.75 m long MMF.Specifically,we demonstrate that a deep convolutional neural network(CNN)can learn the nonlinear relationships between the amplitude of the speckle pattern(phase information lost)obtained at the output of the fiber and the phase or the amplitude at the input of the fiber.Effectively,the network performs a nonlinear inversion task.We obtained image fidelities(correlations)as high as~98%for reconstruction and~94%for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix.We further show that the network can be trained for transfer learning,i.e.,it can transmit images through the MMF,which belongs to another class not used for training/testing.
文摘We introduce a lock-in method to increase the phase contrast in incoherent differential phase contrast(DPC)imaging.This method improves the phase sensitivity by the analog removal of the background.The use of a smart pixel detector with in-pixel signal demodulation,paired with synchronized switching illumination,provides the basis of a bit-efficient approach to emulate a lock-in DPC.The experiments show an increased sensitivity by a factor of up to 8,as expected from theory,and a reduction of collected data by a factor of 70,for equivalent standard DPC measurements;single-shot sensitivity of 0.7 mrad at a frame rate of 1400 frames per second is demonstrated.This new approach may open the way for the use of incoherent phase microscopy in biological applications where extreme phase sensitivity and millisecond response time are required.
