AI Medical Compendium Journal:
Applied optics

Showing 31 to 40 of 60 articles

Measuring laser beams with a neural network.

Applied optics
A deep neural network (NN) is used to simultaneously detect laser beams in images and measure their center coordinates, radii, and angular orientations. A dataset of images containing simulated laser beams and a dataset of images with experimental la...

Learning feature fusion for target detection based on polarimetric imaging.

Applied optics
We propose a polarimetric imaging processing method based on feature fusion and apply it to the task of target detection. Four images with distinct polarization orientations were used as one parallel input, and they were fused into a single feature m...

Self-supervised stereo depth estimation based on bi-directional pixel-movement learning.

Applied optics
Stereo depth estimation is an efficient method to perceive three-dimensional structures in real scenes. In this paper, we propose a novel self-supervised method, to the best of our knowledge, to extract depth information by learning bi-directional pi...

Incremental learning for detection in X-ray luggage perspective images.

Applied optics
Convolutional neural networks have achieved remarkable results in the detection of X-ray luggage contraband. However, with an increase in contraband classes and substantial artificial transformation, the offline network training method has been unabl...

Polarized light sun position determination artificial neural network.

Applied optics
Our previous work has constructed a polarized light orientation determination (PLOD) artificial neural network. Although a PLOD network can determine the solar azimuth angle, it cannot determine the solar elevation angle. Therefore, this paper propos...

All-day thin-lens computational imaging with scene-specific learning recovery.

Applied optics
Modern imaging optics ensures high-quality photography at the cost of a complex optical form factor that deviates from the portability. The drastic development of image processing algorithms, especially advanced neural networks, shows great promise t...

Color computational ghost imaging by deep learning based on simulation data training.

Applied optics
We present a new color computational ghost imaging strategy using a sole single-pixel detector and training by simulated dataset, which can eliminate the actual workload of acquiring experimental training datasets and reduce the sampling times for im...

Gold-viral particle identification by deep learning in wide-field photon scattering parametric images.

Applied optics
The ability to identify virus particles is important for research and clinical applications. Because of the optical diffraction limit, conventional optical microscopes are generally not suitable for virus particle detection, and higher resolution ins...

Small obstacle size prediction based on a GA-BP neural network.

Applied optics
Accurate and effective acquisition of obstacle size parameters is the basis for environment perception, path planning, and autonomous navigation of mobile robots, and is the key to improve the walking performance of mobile robots. In this paper, a ge...

Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network.

Applied optics
Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT im...