Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

340
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
340
Deconvolution01:20

Deconvolution

255
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...
255
Downsampling01:20

Downsampling

255
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...
255
Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

2.3K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.3K
Upsampling01:22

Upsampling

311
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
311

You might also read

Related Articles

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

Sort by
Same author

Recent Progress of Single-Ion Conducting Polymer Electrolytes for Rechargeable Mono- and Multivalent Cation-Based Metal Batteries.

Angewandte Chemie (International ed. in English)·2026
Same author

Photoredox-catalysed cyclisation of carbyne equivalents with alkenes and aldehydes to dihydrofurans.

Chemical communications (Cambridge, England)·2026
Same author

MPLDM: Multi-modal prosthetic loosening diagnostic model for total hip arthroplasty.

Medical image analysis·2025
Same author

Let's make up a story: design and evaluation of an improvisational storytelling robot to foster older adults' meaning and purpose in life.

Frontiers in dementia·2025
Same author

Modular Anti-Counterfeit Tags Formed by Template-Assisted Self-Assembly of Plasmonic Nanocrystals and Authenticated by Machine Learning.

Advanced functional materials·2025
Same author

Towards Better Cephalometric Landmark Detection With Diffusion Data Generation.

IEEE transactions on medical imaging·2025
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
13:13

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Published on: March 19, 2021

3.0K

Reconstructing High Quality Raw Video Using Temporal Affinity and Diffusion Prior.

Wencheng Han, Jianbing Shen, David J Crandall

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |August 7, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new pipeline for reconstructing high-quality RAW videos from sRGB data using just one initial RAW frame. The method addresses storage limitations and computational costs, enabling better RAW video applications.

    More Related Videos

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.8K
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.4K

    Related Experiment Videos

    Last Updated: Sep 12, 2025

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    3.0K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.8K
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.4K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Video Reconstruction

    Background:

    • RAW data offers rich information but faces high storage costs for video.
    • Existing methods struggle with RAW video reconstruction due to computational demands.

    Purpose of the Study:

    • To develop a novel pipeline for high-quality RAW video reconstruction from sRGB data.
    • To overcome limitations of storage and computational cost in RAW video processing.

    Main Methods:

    • Temporal-Affinity Guided De-rendering Network leverages adjacent frames for RAW pixel reference.
    • RAW In-painting Model uses diffusion models to recover missing pixels from sRGB and RAW context.
    • A content-aware video clipper optimizes clip length for storage-quality balance.

    Main Results:

    • The proposed pipeline successfully reconstructs high-quality RAW videos from sRGB data.
    • Experimental results validate the algorithm's accuracy across diverse camera devices and challenging scenarios.
    • A new benchmark for RAW video reconstruction performance evaluation was introduced.

    Conclusions:

    • The novel pipeline effectively reconstructs RAW videos from sRGB data with reduced storage and computational requirements.
    • The introduced benchmark and publicly available resources will advance RAW video research.
    • This work enables wider adoption of RAW video in computer vision applications.