19세기말 애국적 담론과 新小說의 정체성

心臓病診療プラクティス 16. 心不全を癒す

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. In this study, we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference-related spatial artifacts. This neural network-based method is fast to compute and reconstructs phase and amplitude images of the objects using only one hologram, requiring fewer measurements in addition to being computationally faster. We validated this method by reconstructing the phase and amplitude images of various samples, including blood and Pap smears and tissue sections. These results highlight that challenging problems in imaging science can be overcome through machine learning, providing new avenues to design powerful computational imaging systems.

/pdf/phase-recovery-and-holographic-image-reconstruction-using-1ue2w051g5.pdf

Phase recovery and holographic image reconstruction using deep learning in neural networks

Phase recovery from intensity-only measurements forms the heart of coherent imaging techniques and holography. Here we demonstrate that a neural network can learn to perform phase recovery and holographic image reconstruction after appropriate training. This deep learning-based approach provides an entirely new framework to conduct holographic imaging by rapidly eliminating twin-image and self-interference related spatial artifacts. Compared to existing approaches, this neural network based method is significantly faster to compute, and reconstructs improved phase and amplitude images of the objects using only one hologram, i.e., requires less number of measurements in addition to being computationally faster. We validated this method by reconstructing phase and amplitude images of various samples, including blood and Pap smears, and tissue sections. These results are broadly applicable to any phase recovery problem, and highlight that through machine learning challenging problems in imaging science can be overcome, providing new avenues to design powerful computational imaging systems.

/pdf/phase-recovery-and-holographic-image-reconstruction-using-5clpneh94w.pdf

Spatial differentiation is important in image-processing applications such as image sharpening and edge-based segmentation. In these applications, of particular importance is the Laplacian, the simplest isotropic derivative operator in two dimensions. Spatial differentiation can be implemented electronically. However, in applications requiring real-time and high-throughput image differentiation, conventional digital computations become challenging. Optical analog computing may overcome this challenge by offering high-throughput low-energy-consumption operations using compact devices. However, previous works on spatial differentiation with nanophotonic structures are restricted to either one-dimensional differentiation or reflection mode, whereas operating in the transmission mode is important because it is directly compatible with standard image processing/recognition systems. Here, we show that the Laplacian can be implemented in the transmission mode by a photonic crystal slab device. We theoretically derive the criteria for realizing the Laplacian using the guided resonances in a photonic crystal slab. Guided by these criteria, we show that the Laplacian can be implemented using a carefully designed photonic crystal slab with a non-trivial isotropic band structure near the Γ point. Our work points to new opportunities in optical analog computing as provided by nanophotonic structures.

Photonic crystal slab Laplace operator for image differentiation

Multiple scattering of waves in disordered media is a nightmare whether it is for detection or imaging purposes. So far, the best approach to get rid of multiple scattering is optical coherence tomography. This basically combines confocal microscopy and coherence time gating to discriminate ballistic photons from a predominant multiple scattering background. Nevertheless, the imaging-depth range remains limited to 1 mm at best in human soft tissues because of aberrations and multiple scattering. We propose a matrix approach of optical imaging to push back this fundamental limit. By combining a matrix discrimination of ballistic waves and iterative time reversal, we show, both theoretically and experimentally, an extension of the imaging-depth limit by at least a factor of 2 compared to optical coherence tomography. In particular, the reported experiment demonstrates imaging through a strongly scattering layer from which only 1 reflected photon out of 1000 billion is ballistic. This approach opens a new route toward ultra-deep tissue imaging.

https://advances.sciencemag.org/content/advances/2/11/e1600370.full.pdf

Smart optical coherence tomography for ultra-deep imaging through highly scattering media

We demonstrate experimentally that the traditional double random phase encoding (DPRE) technique is vulnerable to the cyphertext-only attack (COA). With the statistical ergodic property of the speckle, we show that the plaintext image can be recovered from the cyphertext alone owing to the fact that their energy spectral density functions are identical. Our result reveals the most serious security issue with the DRPE as it suggests that even the one-time-pad does not guarantee its security. This will open up new inside understanding of current optical security techniques.

Cyphertext-only attack on the double random-phase encryption: Experimental demonstration

Typical methods of automatic estimation of focusing in digital holography calculate every single reconstructed frame to get a critical function and then ascertain the focal plane by finding the extreme value of that function. Here, we propose a digital holographic autofocusing method that computes the focused distance using the first longitudinal difference of the magnitude of the reconstructed image. We demonstrate the proposed method with both numerical simulations and optical experiments of amplitude-contrast and phase-contrast objects. The results suggest that the proposed method performs better than other existing methods, in terms of applicability and computation efficiency, with potential applications in industrial and biomedical inspections where automatic focus tracking is necessary.

Fast autofocusing in digital holography using the magnitude differential.

When monochromatic light is scattered from an optically rough surface a
 complicated three-dimensional (3D) field is generated. These fields are often
 described by reference to the 3D volume (extent) of their speckles, leading to the
 definition of lateral (x,y) and longitudinal speckle sizes (z). For reasons of
 mathematical simplicity the longitudinal speckle size is often derived by examining
 the decorrelation of the speckle field for a single point lying on axis, i.e.,
 x=y=0, and this size is generally assumed to be representative for other speckles
 that lie further off-axis. Some recent theoretical results, however, indicate that
 in fact longitudinal speckle size gets smaller as the observation position moves to
 off-axis spatial locations. In this paper (Part I), we review the physical argument
 leading to this conclusion and support this analysis with a series of robust
 numerical simulations. We discuss, in some detail, computational issues that arise
 when simulating the propagation of speckle fields numerically, showing that the
 spectral method is not a suitable propagation algorithm when the autocorrelation of
 the scattering surface is assumed to be delta correlated. In Part II [J. Opt. Soc.
 Am. A28, 1904 (2011)] of this paper, experimental results are
 provided that exhibit the predicted variation of longitudinal speckle size as a
 function of position in x and y. The results are not only of theoretical interest
 but have practical implications, and in Part II a method for locating the optical
 system axis is proposed and experimentally demonstrated.

/pdf/three-dimensional-static-speckle-fields-part-i-theory-and-4y9zhvyndr.pdf

Dayan Li

Papers

Smart optical coherence tomography for ultra-deep imaging through highly scattering media

Cyphertext-only attack on the double random-phase encryption: Experimental demonstration

Fast autofocusing in digital holography using the magnitude differential.

Smart optical coherence tomography for ultra-deep imaging through highly scattering media

Three-dimensional static speckle fields. Part I. Theory and numerical investigation