Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Advances in Digital Holography, 3-D Imaging, and Optical Information Processing: introduction.

Biomedical optics express·2026

Same author

3D-DeepZern: a deep convolutional neural network for tomographic reconstruction based on Zernike polynomials.

Applied optics·2026

Same author

Advances in digital holography, 3-D imaging, and optical information processing: introduction.

Applied optics·2026

Same author

Advances in digital holography, 3-D imaging, and optical information processing: introduction.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026

Same author

Chronic Electronic Cigarette Exposure Promotes Atherosclerosis and Chondrogenic Modulation of Smooth Muscle Cells.

bioRxiv : the preprint server for biology·2026

Same author

Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease risk.

Nature communications·2026

Same journal

Multifunctional reconfigurable terahertz metasurface based on vanadium dioxide phase transition: achieving broadband absorption and efficient polarization conversion.

Applied optics·2026

Same journal

High-Q-factor electromagnetically induced transparency utilizing quasi-bound states in the continuum in an all-dielectric terahertz metasurface.

Applied optics·2026

Same journal

Automated stitching interferometry for high-precision metrology of X-ray mirrors.

Applied optics·2026

Same journal

Experimental demonstration of an approach to designing a metal-dielectric DBR resonant cavity structure.

Applied optics·2026

Same journal

High-precision wavefront reconstruction from a single-shot interferogram using a physics-driven hybrid feature calibration network.

Applied optics·2026

Same journal

Ultra-high-Q Fano resonance based on coupled topological corner states in Kagome photonic crystals.

Applied optics·2026

See all related articles

Search research articles

Home
Deep Learning Caustic Image Generation.

Home
Deep Learning Caustic Image Generation.

Related Experiment Video

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep learning caustic image generation.

Trieu Nguyen, Quang Trieu, George Nehmetallah

|April 24, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a machine learning framework to generate complex light patterns called caustics. The data-driven approach uses neural networks for real-time, high-quality caustic image synthesis, improving efficiency in optics and design.

More Related Videos

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Related Experiment Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Area of Science:

Computer Graphics
Computational Optics
Machine Learning

Background:

Caustics are intricate light patterns crucial for rendering, design, and optics.
Conventional methods for synthesizing caustics are computationally intensive and do not scale well with increasing complexity.

Purpose of the Study:

To develop a novel, efficient, and data-driven framework for caustic pattern generation.
To replace computationally expensive traditional methods with a machine learning approach.

Main Methods:

A neural network was trained to learn the relationship between transparent object geometry, illumination, and caustic patterns.
The framework utilizes a data-driven approach, leveraging machine learning instead of explicit optimization.

Main Results:

The proposed model generates high-resolution, physically plausible caustic images in real time.
The neural network demonstrates efficient and accurate caustic synthesis across diverse and complex scenarios.

Conclusions:

The machine learning framework offers a practical and efficient alternative for generating caustics.
This approach has significant implications for real-time graphics applications and optical design.