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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

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Imaging through dynamic scattering media with an adapter-enhanced diffusion model.

Cheng Tang, Haishan Liu, Luying Zhang

|November 11, 2025

Summary

This summary is machine-generated.

This study introduces an adapter-enhanced diffusion model (ADM) for clearer images through scattering media. ADM achieves superior performance with limited data, outperforming existing methods in diverse real-world conditions.

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Area of Science:

Optical imaging
Computer vision
Machine learning

Background:

Reconstructing objects obscured by scattering media is a significant challenge in optical imaging.
Current deep learning approaches often demand extensive training datasets and exhibit poor generalization across varied scattering environments.

Purpose of the Study:

To develop a novel deep learning model for robust object reconstruction in scattering media, addressing limitations of existing methods regarding data requirements and generalization.
To introduce an adapter-enhanced diffusion model (ADM) capable of effective cross-domain alignment with minimal training data.

Main Methods:

An adapter-enhanced diffusion model (ADM) was developed, integrating diffusion model denoising with a test-time adapter for cross-domain alignment.
The model was trained on a limited dataset (100 paired images) from a single indoor scattering condition.
Performance was evaluated against classical Retinex optimization, U-Net, SwinUNet, DescatterNet, and a baseline diffusion model across various scattering conditions.

Main Results:

ADM significantly surpassed all compared methods in image quality across diverse scattering scenarios, including higher concentrations and different media (fog, milk).
The model demonstrated remarkable data efficiency, achieving performance comparable to U-Net models trained with 12 times more data.
Attention map analysis indicated that the test-time adapter effectively guided the diffusion process, enabling robust generalization from limited training data.

Conclusions:

The adapter-enhanced diffusion model (ADM) presents a highly efficient and promising solution for imaging through complex scattering media.
ADM's ability to generalize across varied conditions with limited training data makes it suitable for real-world applications.
The study underscores the potential of combining diffusion models with adaptive mechanisms for challenging imaging tasks.