AI Medical Compendium Journal:
Optics letters

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Predicting the eigenstructures of metamaterials with QR-code meta-atoms by deep learning.

Optics letters
Deep neural networks (DNNs) facilitate the reverse design of metamaterial perfect absorbers (MPAs), usually by predicting the MPA structure from the input absorptivity. However, this suffers from the difficulty that the spectrum that actually exists ...

Image reconstruction with transformer for mask-based lensless imaging.

Optics letters
A mask-based lensless camera optically encodes the scene with a thin mask and reconstructs the image afterward. The improvement of image reconstruction is one of the most important subjects in lensless imaging. Conventional model-based reconstruction...

Orthogonality of diffractive deep neural network.

Optics letters
Some rules of the diffractive deep neural network (DNN) are discovered. They reveal that the inner product of any two optical fields in DNN is invariant and the DNN acts as a unitary transformation for optical fields. If the output intensities of the...

Deep-learning-based 3D object salient detection via light-field integral imaging.

Optics letters
This Letter proposes an effective light-field 3D saliency object detection (SOD) method, which is inspired by the idea that the spatial and angular information inherent in a light-field implicitly contains the geometry and reflection characteristics ...

Optical random micro-phase-shift DropConnect in a diffractive deep neural network.

Optics letters
The formulation and training of unitary neural networks is the basis of an active modulation diffractive deep neural network. In this Letter, an optical random phase DropConnect is implemented on an optical weight to manipulate a jillion of optical c...

Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation: erratum.

Optics letters
We present an erratum to our Letter [Opt. Lett.47, 802 (2022)10.1364/OL.448571]. This erratum corrects an error in the sign of one of the higher-order dispersion coefficient used in the simulations of Figs. 2 and 4, as well as in Figs. S1 and S3. The...

3D k-space reflectance fluorescence tomography via deep learning.

Optics letters
We report on the potential to perform image reconstruction in 3D k-space reflectance fluorescence tomography (FT) using deep learning (DL). Herein, we adopt a modified AUTOMAP architecture and develop a training methodology that leverages an open-sou...

Deep learning-enhanced, open-source eigenmode expansion.

Optics letters
We present an open-source eigenmode expansion (EME) software package entirely implemented in the Python programming language. Eigenmode expansion Python (EMEPy) utilizes artificial neural networks to reproduce electromagnetic eigenmode field profiles...

Multi-level spatial details cross-extraction and injection network for hyperspectral pansharpening.

Optics letters
Hyperspectral (HS) pansharpening, which fuses the HS image with a high spatial resolution panchromatic (PAN) image, provides a good solution to overcome the limitation of HS imaging devices. However, most existing convolutional neural network (CNN)-b...

Image-free multi-character recognition.

Optics letters
The recently developed image-free sensing technique decouples semantic information directly from compressed measurements without image reconstruction, which maintains the advantages of both the light hardware and software. However, the existing attem...