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

Downsampling01:20

Downsampling

713
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...
713
Upsampling01:22

Upsampling

646
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...
646
Reducing Line Loss01:18

Reducing Line Loss

396
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
396
Deconvolution01:20

Deconvolution

603
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...
603
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

393
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
393
Linearization and Approximation01:26

Linearization and Approximation

79
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
79

You might also read

Related Articles

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

Sort by
Same author

Ultrasensitive Optical Detection and Elimination of Residual Microtumors with a Postoperative Implantable Hydrogel Sensor for Preventing Cancer Recurrence.

Advanced materials (Deerfield Beach, Fla.)·2024
Same author

Calcium signaling mediates proliferation of the precursor cells that give rise to the ciliated left-right organizer in the zebrafish embryo.

Frontiers in molecular biosciences·2023
Same author

An Ultrathin Composite Polymer Electrolyte Dual-Reinforced by a Polymer of Intrinsic Microporosity (PIM-1) and Poly(tetrafluoroethylene) (PTFE) Porous Membrane.

Small (Weinheim an der Bergstrasse, Germany)·2023
Same author

YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information.

Sensors (Basel, Switzerland)·2023
Same author

Association between transcutaneous oxygen saturation within 24 h of admission and mortality in critically ill patients with non-traumatic subarachnoid hemorrhage: a retrospective analysis of the MIMIC-IV database.

Frontiers in neurology·2023
Same author

Small-cell neuroendocrine carcinoma of the ovary with unusual uterine and rectal metastases.

Asian journal of surgery·2023
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

Multi-Branch Tree-based Fusion Neural Architecture Search with Zero-Cost Screen for Multi-Modal Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Feb 17, 2026

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

18.2K

L0 -Regularized Image Downscaling.

Junjie Liu, Shengfeng He, Rynson W H Lau

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 9, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new image downscaling method using L1-regularized optimization. The approach enhances edge preservation and visual quality by employing novel gradient-ratio and downsampling priors, outperforming existing techniques.

    More Related Videos

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.8K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    835

    Related Experiment Videos

    Last Updated: Feb 17, 2026

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    18.2K
    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    6.8K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    835

    Area of Science:

    • Computer Vision
    • Image Processing
    • Optimization

    Background:

    • Image downscaling is crucial for various applications but often results in loss of detail and visual quality.
    • Existing methods struggle to effectively preserve salient edges and overall visual perception during downscaling.

    Purpose of the Study:

    • To propose a novel L1-regularized optimization framework for superior image downscaling.
    • To enhance the preservation of salient edges and visual perception in downscaled images.
    • To improve the robustness and quality of image downscaling algorithms.

    Main Methods:

    • Development of a new L1-regularized optimization framework for image downscaling.
    • Introduction of a gradient-ratio prior incorporating L1-norm sparsity to preserve salient edges.
    • Implementation of a downsampling prior to optimize pixel estimation based on neighboring pixels.

    Main Results:

    • The proposed algorithm demonstrates superior performance on Urban100 and BSDS500 datasets.
    • Achieved state-of-the-art results in terms of image quality and robustness.
    • Successfully preserved salient edges and visual perception in downscaled images.

    Conclusions:

    • The novel L1-regularized optimization framework effectively addresses limitations in current image downscaling techniques.
    • The proposed gradient-ratio and downsampling priors significantly improve downscaling performance.
    • The algorithm offers a robust and high-quality solution for image downscaling tasks.