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

Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

Depth Perception and Spatial Vision

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

You might also read

Related Articles

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

Sort by

Same author

Single-exposure holographic lithography of ultra-high aspect-ratio microstructures.

Nature communications·2026

Same author

Computational wide-field imaging of poplar embolism and wound-response with a deployable microscope.

The Review of scientific instruments·2025

Same author

Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe.

Optics continuum·2023

Same author

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Optics express·2022

Same author

Needle-based deep-neural-network camera.

Applied optics·2021

Same author

Optics-free imaging of complex, non-sparse and color QR-codes with deep neural networks.

OSA continuum·2020

Same journal

Single-scan adaptive optics-enabled quantitative optical coherence tomography angiography for absolute three-dimensional retinal blood flow mapping.

Optica·2026

Same journal

Longitudinal, label-free, high-resolution imaging of glioblastoma spheroid response to therapy: a translational tool for preclinical evaluation of chemotherapy, radiation, and immunotherapy.

Optica·2026

Same journal

Enhanced penetration depth in optical coherence tomography and photoacoustic microscopy <i>in vivo</i> enabled by absorbing dye molecules.

Optica·2026

Same journal

Single-photon avalanche diode imaging sensor for subsurface fluorescence LiDAR.

Optica·2026

Same journal

Space-time inverse-scattering of translation-based motion.

Optica·2025

Same journal

Ultrasensitive Alzheimer's disease biomarker detection with nanopillar photonic crystal biosensors.

Optica·2025

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 25, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Bijective-constrained cycle-consistent deep learning for optics-free imaging and classification.

Soren Nelson¹, Rajesh Menon¹

¹Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah 84112, USA.

|June 28, 2023

Summary

This summary is machine-generated.

This study introduces a new deep learning model for optics-free imaging. It learns both forward and inverse models, enabling thinner cameras and diverse image reconstruction from bare image sensors.

More Related Videos

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Related Experiment Videos

Last Updated: Jul 25, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Computational imaging
Deep learning
Computer vision

Background:

Data-driven deep learning methods excel in computational imaging but struggle with structurally diverse images from bare image sensors.
Existing approaches often rely solely on data, lacking constraints for robust reconstruction.

Purpose of the Study:

To develop a self-consistent supervised model for improved optics-free image reconstruction.
To constrain deep learning predictions by learning both the forward and inverse imaging models.
To enable the development of ultra-thin cameras.

Main Methods:

Proposed a self-consistent supervised deep learning model.
Incorporated cycle consistency alongside traditional reconstruction losses.
Trained the network to model an ideal bijective imaging system.

Main Results:

Successfully reconstructed structurally diverse target images from raw, optics-free sensor data.
Demonstrated the necessity of both cycle consistency and reconstruction losses for incoherent optics-free imaging.
Achieved imaging capabilities with a significantly reduced camera profile.

Conclusions:

The proposed model effectively addresses limitations of purely data-driven methods in optics-free imaging.
Learning the forward model alongside the inverse provides crucial constraints for accurate reconstruction.
This approach paves the way for novel, ultra-thin camera designs.