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  3. Selection, Aggregation, And Enhancement: Trajectory Consistent Diffusion Model For Image Super-resolution.
  1. Home
  3. Selection, Aggregation, And Enhancement: Trajectory Consistent Diffusion Model For Image Super-resolution.

Related Experiment Video

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Selection, Aggregation, and Enhancement: Trajectory Consistent Diffusion Model for Image Super-Resolution.

Detian Huang, Yaohui Guo, Yuting Huang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 10, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a trajectory consistent diffusion model (TCDM) for image super-resolution (ISR). TCDM enhances structural consistency and fine details by optimizing the sampling process without altering the core diffusion model.

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    Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence

    Background:

    • Diffusion models show potential for image super-resolution (ISR).
    • Existing methods often underutilize pretrained backbones and lack sampling trajectory constraints, leading to reduced structural consistency and detail.
    • This limits the fidelity and detail of reconstructed images.

    Purpose of the Study:

    • To introduce a novel Trajectory Consistent Diffusion Model (TCDM) for image super-resolution.
    • To enhance structural consistency and fine details in super-resolved images.
    • To optimize the sampling process using lightweight components and inference-time strategies while keeping the diffusion backbone frozen.

    Main Methods:

    • Dynamic Semantic Selection (DSS): Records early intermediates, matches them to upsampled low-resolution features, and reconditions sampling to minimize conditioning-noise scale mismatch.
  • Cross-Step Aggregation Guidance (CAG): Aggregates current state features with selected intermediates to ensure trajectory-level consistency in noise prediction.
  • Frequency Enhancement Adapter (FE-Adapter): A plug-and-play module that injects frequency-domain cues during training to improve high-frequency perception and preserve global structures.

    • Main Results:

    • TCDM achieves high-fidelity, detail-rich image reconstructions.
    • Demonstrates strong structural fidelity and competitive no-reference perceptual quality across multiple ISR benchmarks.
    • Offers a favorable trade-off between image fidelity and perceptual quality.

    Conclusions:

    • TCDM effectively addresses limitations of current diffusion-based ISR methods by optimizing the sampling trajectory.
    • The proposed DSS, CAG, and FE-Adapter mechanisms contribute to improved structural consistency and detail preservation.
    • TCDM presents a promising approach for high-quality image super-resolution.