Deep learning · Multi-modality image fusion 深度学习,多模态图像融合 核心思想 训练了一个微型配准模块($\mathcal R$)预测输入图像的变形场,解决问题1 设计了一个循环并行扩张卷积层(PDC),解决问题2 参考链接 [什么是图像融合?(一看就通,通俗易懂)] 网络结构 作者提出的网络结构如下所示。一眼看过去有点乱...
# 图像融合论文阅读:CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion @inproceedings{zhao2023cddfuse, title={Cddfuse: Correlation-driven dual-branch fea…
Multi-modality image fusion method based on region and human eye contrast sensitivity characteristic基于区域和人眼对比敏感特性的异源图像融合方法,包含如下步骤:(1)对待融合的源图像分别用非下采样Contourlet变换NSCT进行多尺度分解,得到源图像的各阶子带系数;(2)根据人眼视觉对比度函数LCSF,人眼视觉绝对对比度灵敏...
论文假设是,在MMIF任务中,两个模态的输入特征在低频时是相关的,代表模态共享的信息,而高频特征是不相关的,代表各自模态的独特特征。 CDDFuse包含四个模块,即双分支编码器用于特征提取和分解,解码器用于重建原始图像(在训练阶段I)或生成融合图像(在训练阶段II),以及基础/细节融合层分别用于融合不同频率的特征。 训练...
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and texture details. To leverage strong generative priors and address challenges such as unstable training and lack of ...
Multi-modality image fusion (MMIF) aims to integrate complementary information from different modalities into a single fused image to represent the imaging scene and facilitate downstream visual tasks comprehensively. In recent years, significant progress has been made in MMIF tasks due to advances in ...
modal image fusion.It provides a feasible paradigm for diffusion model in lacking ground truth.It avoids unstable training of GAN-based fusion methods.It has better flexibility and less cost than existing diffusion-based methods.Our method exhibits good fusion results and excellent semantic ...
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion Zixiang Zhao1,2 Haowen Bai1 Yuanzhi Zhu2 Jiangshe Zhang1∗ Shuang Xu3 Yulun Zhang2 Kai Zhang2 Deyu Meng1,5 Radu Timofte2,4 Luc Van Gool2 1Xi'an Jiaotong University 2Computer Vision Lab, ETH ...
In recent years, Multi-Modality Image Fusion (MMIF) has been applied to many fields, which has attracted many scholars to endeavour to improve the fusion performance. However, the prevailing focus has predominantly been on the architecture design, rather than the training strategies. As a low-lev...
本文的研究目的是提出一种新颖的多模态图像融合网络,名为CDDFuse(Correlation-Driven Dual-Branch Feature Decomposition Fusion),用于处理不同模态图像融合的挑战。具体来说,CDDFuse旨在生成融合图像,同时保留不同模态的优势,例如在红外图像中保留热辐射信息和在可见光图像中保留详细的纹理信息。该网络通过建模跨模态特征...