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

Assessment of Diffusion and Perfusion01:17

Assessment of Diffusion and Perfusion

1.5K
Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Research Progress in Field Grading Materials for New Power Systems.

Molecules (Basel, Switzerland)·2026
Same author

Strigolactone-mediated architecture regulation and stress resilience: Insights and innovations for crop breeding.

Journal of integrative plant biology·2026
Same author

Genome-edited rice variety with low-cadmium accumulation in the grain.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Chloroplast sunscreening by protein condensates confers high-light tolerance.

Cell·2026
Same author

Nonlinear Electrical Conductivity and Thermal Conductivity of g-C<sub>3</sub>N<sub>4</sub>/Liquid Silicone Rubber Field Grading Composites.

Materials (Basel, Switzerland)·2026
Same author

Different direction adversarial sample for diffusion model.

Neural networks : the official journal of the International Neural Network Society·2026

Related Experiment Video

Updated: Jan 8, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

723

Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach.

Jiayang Li, Chengjie Jiang, Junjun Jiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 11, 2025
    PubMed
    Summary

    DiTFuse, an instruction-driven Diffusion Transformer, enhances image fusion by enabling semantic awareness and user control. This novel framework achieves robust, adaptable, and controllable fusion across various modalities without ground-truth data.

    More Related Videos

    Hybrid &#181;CT-FMT imaging and image analysis
    13:45

    Hybrid µCT-FMT imaging and image analysis

    Published on: June 4, 2015

    13.6K
    Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
    05:41

    Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

    Published on: February 9, 2024

    1.0K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    723
    Hybrid &#181;CT-FMT imaging and image analysis
    13:45

    Hybrid µCT-FMT imaging and image analysis

    Published on: June 4, 2015

    13.6K
    Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
    05:41

    Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis

    Published on: February 9, 2024

    1.0K

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Existing image fusion methods lack robustness, adaptability, and user control, especially in challenging conditions like low-light or color shifts.
    • Current fusion networks are task-specific and struggle to incorporate high-level semantic understanding or user intent.
    • The absence of ground-truth fused images and limited dataset sizes hinder the training of end-to-end models for complex fusion tasks.

    Purpose of the Study:

    • To introduce DiTFuse, a novel instruction-driven Diffusion Transformer framework for end-to-end, semantics-aware image fusion.
    • To enable hierarchical and fine-grained control over fusion dynamics using natural language instructions.
    • To overcome limitations of pre- and post-fusion pipelines by integrating semantic understanding directly into the fusion process.

    Main Methods:

    • DiTFuse jointly encodes images and natural language instructions in a shared latent space for instruction-driven fusion.
    • A multi-degradation masked-image modeling strategy trains the network for cross-modal alignment and modality-invariant restoration without ground truth.
    • A curated instruction dataset facilitates interactive fusion capabilities and zero-shot generalization to new fusion scenarios.

    Main Results:

    • DiTFuse demonstrates superior quantitative and qualitative performance on infrared-visible, multi-focus, and multi-exposure fusion benchmarks.
    • The framework achieves sharper textures and improved semantic retention compared to existing methods.
    • Experiments confirm DiTFuse's ability to support multi-level user control and generalize to instruction-conditioned segmentation tasks.

    Conclusions:

    • DiTFuse offers a unified, instruction-driven approach to image fusion, significantly advancing robustness, adaptability, and controllability.
    • The model effectively integrates semantic understanding and user intent, overcoming key limitations of prior fusion techniques.
    • DiTFuse presents a versatile architecture capable of handling diverse fusion tasks and enabling novel applications like instruction-conditioned segmentation.