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

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
State Space to Transfer Function01:21

State Space to Transfer Function

The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:

You might also read

Related Articles

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

Sort by
Same author

Hybrid zeolitic imidazolate frameworks with catalytically active TO4 building blocks.

Angewandte Chemie (International ed. in English)·2010
Same author

Whiter matter abnormalities in medication-naive subjects with a single short-duration episode of major depressive disorder.

Psychiatry research·2010
Same author

A new comorbidity index: the health-related quality of life comorbidity index.

Journal of clinical epidemiology·2010
Same author

S-adenosylmethionine inhibits the growth of cancer cells by reversing the hypomethylation status of c-myc and H-ras in human gastric cancer and colon cancer.

International journal of biological sciences·2010
Same author

Nano-sized SnSbAgx alloy anodes prepared by reductive co-precipitation method used as lithium-ion battery materials.

Journal of nanoscience and nanotechnology·2010
Same author

Complementary diffusion tensor imaging study of the corpus callosum in patients with first-episode and chronic schizophrenia.

Journal of psychiatry & neuroscience : JPN·2010

Related Experiment Video

Updated: May 24, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

SGCM-Net: structure-guided mural inpainting with state space model.

Yiyin Qiu1, Jianjun Chen2, Yan Fan3

  • 1School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212100, China.

Scientific Reports
|May 22, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Convolutional Neural Network (CNN)-Mamba hybrid for digital image inpainting, improving mural restoration by effectively handling complex textures and ensuring global consistency in degraded artworks.

Related Experiment Videos

Last Updated: May 24, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

Area of Science:

  • Digital image restoration
  • Artificial intelligence in art conservation
  • Computer vision for cultural heritage

Background:

  • Degraded ancient murals present restoration challenges due to intricate textures and curves.
  • Traditional restoration methods are labor-intensive, while existing digital image inpainting techniques, particularly Convolutional Neural Network (CNN)-based ones, struggle with global consistency on complex structures.
  • Limited receptive fields in CNNs hinder their ability to capture long-range dependencies crucial for realistic inpainting.

Purpose of the Study:

  • To propose a novel CNN-Mamba hybrid inpainting architecture for restoring degraded ancient murals.
  • To address the limitations of existing methods in handling complex textures and maintaining global consistency.
  • To improve the accuracy and visual coherence of digital image inpainting for cultural heritage applications.

Main Methods:

  • A two-stage task decomposition paradigm is employed for mural inpainting.
  • Structure-Guided Fusion Blocks (SGFBs) adaptively integrate structural information from edge inpainting across multiple scales.
  • Multi-Way Mamba Process Blocks (MMPBs), leveraging State Space Models (SSMs), are integrated into the network bottleneck to capture global dependencies with linear complexity.

Main Results:

  • The proposed CNN-Mamba hybrid architecture demonstrates effective restoration of global styles in ancient murals.
  • The method successfully fills in coherent and contextually appropriate details within degraded regions.
  • Evaluations on mural and landscape painting datasets show competitive performance against established inpainting techniques.

Conclusions:

  • The CNN-Mamba hybrid approach offers a significant advancement in digital image inpainting for complex artworks like ancient murals.
  • The integration of Mamba-based State Space Models enhances the capture of global context, leading to improved restoration quality.
  • This method provides a promising solution for preserving and restoring cultural heritage through advanced AI techniques.