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

Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising--Part II:

Onur G Guleryuz1

  • 1DoCoMo Communications Laboratories USA, Inc., San Jose, CA 95110, USA. guleryuz@docomolabs-usa.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 8, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Approximation and compression with sparse orthonormal transforms.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
Same author

Spatial sparsity-induced prediction (SIP) for images and video: a simple way to reject structured interference.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2010
Same author

Weighted averaging for denoising with overcomplete dictionaries.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2007
Same author

Linear, worst-case estimators for denoising quantization noise in transform coded images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2006
Same author

Nonlinear approximation based image recovery using adaptive sparse reconstructions and iterated denoising--Part I: Theory.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2006
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

This study introduces an adaptive algorithm for estimating missing data in nonstationary signals using sparse linear transforms. The method efficiently reconstructs images, even with complex features, by progressing through denoising operations.

Area of Science:

  • Signal Processing
  • Image Reconstruction
  • Data Estimation

Background:

  • Nonstationary signals present challenges for data estimation.
  • Existing algorithms often require complex preconditioning or segmentation.
  • Sparse signal representations are crucial for efficient data recovery.

Purpose of the Study:

  • To develop a powerful, adaptive algorithm for estimating missing data in nonstationary signals.
  • To address the nonconvex nature of nonlinear approximants in signal estimation.
  • To improve image reconstruction in regions with textures and edges.

Main Methods:

  • Combining adaptive techniques with sparse linear transforms for data estimation.
  • Implementing a progressive algorithm based on denoising operations.

Related Experiment Videos

  • Utilizing transforms that yield sparse coefficients over missing data regions.

    • Main Results:

    • The proposed algorithm effectively estimates missing data in nonstationary signals and images.
    • It outperforms established methods on complex image features like textures and edges.
    • The method avoids complex preconditioning, segmentation, or edge detection steps.

    Conclusions:

    • The developed algorithm offers a robust and efficient solution for missing data estimation in nonstationary signals.
    • Adaptive linear transforms are key to handling complex signal components.
    • The progressive denoising approach simplifies the estimation process.