DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion

2021 IEEE/CVF International Conference on Computer Vision (ICCV), Год журнала: 2023, Номер unknown, С. 8048 - 8059

Опубликована: Окт. 1, 2023

Multi-modality image fusion aims to combine different modalities produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors address challenges unstable training lack interpretability for GAN-based methods, we propose a novel algorithm based on denoising diffusion probabilistic model (DDPM). The task is formulated conditional generation problem under DDPM sampling framework, which further divided into an unconditional subproblem maximum likelihood subproblem. latter modeled in hierarchical Bayesian manner with latent variables inferred by expectation-maximization (EM) algorithm. By integrating inference solution iteration, our method can generate high-quality natural cross-modality information from source images. Note all required pre-trained model, no fine-tuning needed. Our extensive experiments indicate approach yields promising results infrared-visible medical fusion. code available at https://github.com/Zhaozixiang1228/MMIF-DDFM.

Язык: Английский

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CasFormer: Cascaded transformers for fusion-aware computational hyperspectral imaging

Information Fusion, Год журнала: 2024, Номер 108, С. 102408 - 102408

Опубликована: Апрель 6, 2024

Язык: Английский

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Diff-IF: Multi-modality image fusion via diffusion model with fusion knowledge prior

Information Fusion, Год журнала: 2024, Номер 110, С. 102450 - 102450

Опубликована: Май 3, 2024

Язык: Английский

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Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Год журнала: 2024, Номер unknown, С. 27016 - 27025

Опубликована: Июнь 16, 2024

Язык: Английский

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Equivariant Multi-Modality Image Fusion

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Год журнала: 2024, Номер abs/2004.10934, С. 25912 - 25921

Опубликована: Июнь 16, 2024

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Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Diagnostics, Год журнала: 2024, Номер 14(2), С. 146 - 146

Опубликована: Янв. 8, 2024

Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source analysis platform, become a valuable tool in education due its integration of various AI applications. Through architecture, students can gain practical experience diverse images the latest technology, reinforcing their understanding anatomy technology while fostering independent learning clinical reasoning skills. The implementation this platform improves instruction quality nurtures skilled professionals who meet demands practice, research institutions, innovation enterprises. algorithms' application processing have facilitated translation from lab applications education.

Язык: Английский

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ASFusion: Adaptive visual enhancement and structural patch decomposition for infrared and visible image fusion

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 132, С. 107905 - 107905

Опубликована: Фев. 1, 2024

Язык: Английский

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CFNet: An infrared and visible image compression fusion network

Pattern Recognition, Год журнала: 2024, Номер 156, С. 110774 - 110774

Опубликована: Июль 14, 2024

Язык: Английский

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A review on infrared and visible image fusion algorithms based on neural networks

Journal of Visual Communication and Image Representation, Год журнала: 2024, Номер 101, С. 104179 - 104179

Опубликована: Май 1, 2024

Infrared and visible image fusion represents a significant segment within the domain. The recent surge in processing hardware advancements, including GPUs, TPUs, cloud computing platforms, has facilitated of extensive datasets from multiple sensors. Given remarkable proficiency neural networks feature extraction fusion, their application infrared emerged as prominent research area years. This article begins by providing an overview current mainstream algorithms for based on networks, detailing principles various algorithms, representative works, respective advantages disadvantages. Subsequently, it introduces domain-relevant datasets, evaluation metrics, some typical scenarios. Finally, conducts qualitative quantitative evaluations results state-of-the-art offers future prospects experimental results.

Язык: Английский

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A degradation-aware guided fusion network for infrared and visible image

Information Fusion, Год журнала: 2025, Номер 118, С. 102931 - 102931

Опубликована: Янв. 8, 2025

Язык: Английский

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