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

Deconvolution01:20

Deconvolution

764
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...
764
Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

708
Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...
708
Upsampling01:22

Upsampling

743
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...
743
Region of Convergence of Laplace Tarnsform01:20

Region of Convergence of Laplace Tarnsform

1.4K
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This...
1.4K
Convolution Properties II01:17

Convolution Properties II

751
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
751
Definition of Laplace Transform01:22

Definition of Laplace Transform

5.0K
The Laplace transform is an indispensable mathematical technique for simplifying the resolution of differential equations by converting them into more manageable algebraic expressions. The Laplace transform of a function is denoted by L[x(t)], where x(t) is the time-domain function. The laplace transform is mathematically expressed as
5.0K

You might also read

Related Articles

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

Sort by
Same author

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
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

DrawMotion: Generating 3D Human Motions by Freehand Drawing.

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

MC#: Mixture Compressor for Mixture-of-Experts Large Models.

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

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

Medical image analysis·2025
Same author

UniVST: A Unified Framework for Training-Free Localized Video Style Transfer.

IEEE transactions on pattern analysis and machine intelligence·2025

Related Experiment Videos

Exposure fusion using boosting Laplacian pyramid.

Jianbing Shen, Ying Zhao, Shuicheng Yan

    IEEE Transactions on Cybernetics
    |August 20, 2014
    PubMed
    Summary

    This study introduces a novel hybrid exposure weight method for image fusion. The approach enhances image quality by preserving color and texture details in static scenes.

    Area of Science:

    • Computer Vision
    • Image Processing

    Background:

    • High dynamic range (HDR) imaging often requires combining multiple exposures.
    • Existing methods struggle with preserving fine details and natural color appearance.

    Purpose of the Study:

    • To develop an advanced exposure fusion approach for high-quality image generation from multiple exposures.
    • To improve upon existing image fusion techniques and tone mapping operators.

    Main Methods:

    • A novel hybrid exposure weight measurement integrating local, global, and just noticeable distortion-based saliency weights.
    • A boosting Laplacian pyramid framework that enhances detail and base signals guided by the hybrid weights.
    • The method considers both individual image exposure levels and relative exposure differences.

    Related Experiment Videos

    Main Results:

    • The proposed approach effectively blends multiple exposure images for static scenes.
    • Preservation of both color appearance and intricate texture structures is achieved.
    • Experimental results show superior performance compared to existing exposure fusion and tone mapping techniques.

    Conclusions:

    • The novel hybrid weight and boosting Laplacian pyramid offer a robust solution for exposure fusion.
    • The method produces visually pleasing images with enhanced color and texture details.
    • This technique advances the field of multi-exposure image fusion.