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 Experiment Videos

Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization.

Weisheng Dong1, Lei Zhang, Guangming Shi

  • 1Key Laboratory of Intelligent Perception and Image Understanding (Chinese Ministry of Education), School of Electronic Engineering, Xidian University, Xi'an, China. wsdong@mail.xidian.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 1, 2011
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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...

You might also read

Related Articles

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

Sort by
Same author

Study on the influence of cementation sequence on the mechanical properties and microstructure of MICP-modified ili loess.

PloS one·2026
Same author

Effects of photochemical and aqueous-phase oxidation on secondary formation during the 31st FISU Summer World University Games, 2023.

Journal of environmental sciences (China)·2026
Same author

Improved Spontaneous EEG Signal Decoding Efficiency by Function Predefined Convolutional Neural Network.

IEEE transactions on neural networks and learning systems·2026
Same author

Learning Retinex Prior for Compressive Hyperspectral Image Reconstruction.

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

Construction and validation of a prognostic risk score model for malignant mesothelioma.

Discover oncology·2026
Same author

Double Nonconvex Tensor Robust Kernel Principal Component Analysis and Its Visual Applications.

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

SinColor: Uncertainty-Guided Single-Step Diffusion for Image Colorization.

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

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

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

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

This study introduces adaptive sparse representation for image restoration. By selecting optimal sparse domains and applying adaptive regularization, the method significantly improves deblurring and super-resolution results.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Sparse representation is effective for image restoration due to its ability to model natural images.
  • Image restoration quality depends on the suitability of the sparse domain for representing image content.
  • Existing methods may not adapt to variations in image content across different patches.

Purpose of the Study:

  • To enhance image restoration by adaptively selecting sparse domains.
  • To introduce adaptive regularization techniques for improved image structure preservation.
  • To optimize the sparse representation framework for superior deblurring and super-resolution.

Main Methods:

  • Learning multiple sets of bases from example image patches for adaptive domain selection.

Related Experiment Videos

  • Integrating autoregressive (AR) models learned from data to regularize local image structures.
  • Incorporating image nonlocal self-similarity as an additional regularization term.
  • Adaptively estimating the sparsity regularization parameter.

    • Main Results:

    • The proposed method demonstrates superior performance in image deblurring and super-resolution.
    • Adaptive sparse domain selection leads to better characterization of local image structures.
    • Adaptive regularization significantly enhances image restoration quality.
    • Achieved improved results in both Peak Signal-to-Noise Ratio (PSNR) and visual perception compared to state-of-the-art algorithms.

    Conclusions:

    • Adaptive sparse domain selection and regularization are crucial for high-quality image restoration.
    • The proposed framework offers a robust approach for various image restoration tasks.
    • The method outperforms existing algorithms, providing better visual fidelity and quantitative metrics.