Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

Nonlocal hierarchical dictionary learning using wavelets for image denoising.

Ruomei Yan, Ling Shao, Yan Liu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 20, 2013

Summary

This summary is machine-generated.

Related Concept Videos

Deconvolution

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

Impact of adjuvant breast radiotherapy on the risk and the survival of second primary lung cancer: a large population-based study.

Japanese journal of clinical oncology·2026

Same author

Topology-Preserving Deep Hashing for Ultrafast Drone-Dominated Object Detection.

IEEE transactions on neural networks and learning systems·2026

Same author

High-throughput screening of EGFR/Ca<sup>2+</sup> signaling modulators in cardiac hypertrophy using a tetrahedral DNA nanostructure-based hESC platform.

Journal of pharmaceutical analysis·2026

Same author

Development and Application of a LAMP Assay for Detecting E198A-Type MBC-Resistant <i>Clarireedia monteithiana</i>.

Plant disease·2026

Same author

Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Method.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

Semi-Negative Contrastive Subclass Discriminative Network for Compositional Zero-Shot Learning.

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

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

See all related articles

This study introduces a novel image denoising method using nonlocal dictionary learning with wavelets. The approach effectively handles high noise levels and uniform noise, outperforming existing techniques.

Area of Science:

Computer Vision
Image Processing
Signal Processing

Background:

Image denoising is crucial for representation models.
Current methods rely on fixed representations and struggle with high noise or non-Gaussian noise.
Image self-similarity is a key exploited feature in state-of-the-art denoising.

Purpose of the Study:

To develop an adaptive image denoising method.
To improve performance at high noise levels and for uniform noise.
To leverage wavelet sparsity and nonlocal dictionary learning.

Main Methods:

Employed nonlocal dictionary learning at each wavelet decomposition level.
Utilized the multiresolution structure and sparsity inherent in wavelets.
Focused on exploiting image self-similarity within the wavelet domain.

Related Experiment Videos

Main Results:

The proposed method demonstrated superior performance compared to two state-of-the-art algorithms at higher noise levels.
The approach showed increased adaptability to uniform noise, a less researched area.
Effective denoising was achieved by combining wavelet sparsity and nonlocal learning.

Conclusions:

Nonlocal dictionary learning within wavelet decomposition offers a robust image denoising solution.
The method provides significant advantages for challenging noise conditions, including high levels and uniform distributions.
This technique advances adaptive image denoising capabilities.