A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam.This gives rise to confocal,im...A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam.This gives rise to confocal,image scanning,and structured illumination microscopy methods.On the other hand,as shown recently by Tsang and others,subdiffractional resolution at the detection end of the microscope can be achieved by replacing the intensity measurement in the image plane with spatial mode demultiplexing.In this work,we study the combined action of Tsang’s method with image scanning.We experimentally demonstrate superior lateral resolution and enhanced image quality compared to either method alone.This result paves the way for integrating spatial demultiplexing into existing microscopes,contributing to further pushing the boundaries of optical resolution.展开更多
We propose a practical scheme for end-to-end optical backpropagation in neural networks. Using saturable absorption for the nonlinear units, we find that the backward-propagating gradients required to train the networ...We propose a practical scheme for end-to-end optical backpropagation in neural networks. Using saturable absorption for the nonlinear units, we find that the backward-propagating gradients required to train the network can be approximated in a surprisingly simple pump-probe scheme that requires only simple passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximation. With backpropagation through nonlinear units being an outstanding challenge to the field, this work provides a feasible path toward truly all-optical neural networks.展开更多
基金funded by BBSRC grant BB/X004317/1supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956071supported by the Clarendon Fund scholarship.
文摘A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam.This gives rise to confocal,image scanning,and structured illumination microscopy methods.On the other hand,as shown recently by Tsang and others,subdiffractional resolution at the detection end of the microscope can be achieved by replacing the intensity measurement in the image plane with spatial mode demultiplexing.In this work,we study the combined action of Tsang’s method with image scanning.We experimentally demonstrate superior lateral resolution and enhanced image quality compared to either method alone.This result paves the way for integrating spatial demultiplexing into existing microscopes,contributing to further pushing the boundaries of optical resolution.
文摘We propose a practical scheme for end-to-end optical backpropagation in neural networks. Using saturable absorption for the nonlinear units, we find that the backward-propagating gradients required to train the network can be approximated in a surprisingly simple pump-probe scheme that requires only simple passive optical elements. Simulations show that, with readily obtainable optical depths, our approach can achieve equivalent performance to state-of-the-art computational networks on image classification benchmarks, even in deep networks with multiple sequential gradient approximation. With backpropagation through nonlinear units being an outstanding challenge to the field, this work provides a feasible path toward truly all-optical neural networks.
