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Related Concept Videos

Light Acquisition02:16

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.
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.
Deconvolution01:20

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...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...

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Related Experiment Video

Updated: May 10, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

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Pix2HDR - A Pixel-Wise Acquisition and Deep Learning-Based Synthesis Approach for High-Speed HDR Videos.

Caixin Wang, Jie Zhang, Matthew A Wilson

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 5, 2024
    PubMed
    Summary

    This study introduces a novel method for capturing high-speed, high dynamic range (HDR) video by sampling individual pixels at varied exposures. This technique overcomes limitations of traditional cameras, enabling clear video capture in challenging dynamic scenes.

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    Area of Science:

    • Computer Vision
    • Image Sensors
    • Deep Learning

    Background:

    • Capturing dynamic scenes with wide motion and light intensity is vital for vision applications.
    • Conventional cameras struggle with high-speed, high dynamic range (HDR) video due to frame rate limitations.
    • Existing multi-exposure methods sacrifice speed and can introduce artifacts from motion misalignment.

    Purpose of the Study:

    • To develop a method for acquiring high-speed HDR video that overcomes the trade-offs of current techniques.
    • To enable accurate capture of scenes with both fast motion and extreme lighting variations.
    • To improve the adaptability and performance of vision systems in dynamic environments.

    Main Methods:

    • Implemented a pixel-wise programmable image sensor for individual pixel sampling with varying exposures and phase offsets.
    • Developed a deep neural network approach to transform pixel-wise outputs into HDR video using end-to-end learned weights.
    • Utilized a novel sampling pattern to capture fast motion and high dynamic range simultaneously.

    Main Results:

    • Achieved aliasing-free HDR video acquisition at 1000 frames per second (FPS).
    • Successfully resolved fast motion in low-light conditions and against bright backgrounds.
    • Demonstrated minimized motion blurring and high spatiotemporal resolution in captured videos.

    Conclusions:

    • The proposed pixel-wise sampling combined with deep neural networks significantly enhances HDR video acquisition capabilities.
    • This method addresses critical challenges in capturing dynamic scenes, outperforming conventional cameras.
    • The approach offers greater adaptability and performance for vision systems operating under demanding conditions.