系列博客目录

---

---

1、Here are some key papers related to multimodal denoising that may be relevant to your research:

1. Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals

   • Authors: P Thiam, HA Kestler, F Schwenker
   • Summary: This paper discusses a multimodal deep denoising convolutional autoencoder (DDCAE) for pain intensity classification using physiological signals. The approach highlights how multimodal information fusion can improve the accuracy of classification by reducing noise in the data.
   • Link: ResearchGate PDF

2. Multimodal Data Visualization, Denoising and Clustering with Integrated Diffusion

   • Authors: MR Kuchroo, A Godhavarthi, G Wolf
   • Summary: This paper introduces an integrated diffusion method to combine multimodal datasets for data denoising, visualization, and clustering. The technique proves useful in reducing noise across diverse multimodal data and enhancing the clarity of visualizations.
   • Link: ICML Preprint PDF

3. Deep CNN Model for Multimodal Medical Image Denoising

   • Authors: W El-Shafai, A Mahmoud, A Ali, E El-Rabaie
   • Summary: This study presents a CNN-based denoising model for multimodal medical images. The authors demonstrate that their deep CNN model effectively reduces noise across different types of medical imaging modalities, offering substantial improvements in image quality.
   • Link: ResearchGate PDF

These papers showcase different applications of multimodal denoising in areas such as medical imaging and signal classification, and they offer insights into methodologies like deep learning and integrated diffusion.

2、being 搜 Multimodal denoising

Multi-modal deep convolutional dictionary learning for image denoising

摘要

利用传统字典学习（DicL）的能力，并借鉴深度神经网络（DNNs）的成功，最近提出的深度卷积字典学习（DCDicL）框架在图像去噪中展现了卓越的表现。需要注意的是，DCDicL方法的应用仅限于单模态场景，而在实际中，图像通常来自多种不同的模态。为扩展DCDicL方法的应用范围，本文设计了其多模态版本，命名为MMDCDicL。具体来说，在MMDCDicL的数学模型中，我们采用一种分析方法来处理与指导模态相关的子问题，利用其固有的可靠性。同时，像DCDicL一样，我们为噪声模态采用基于网络的学习方法，从数据中提取可信的信息。在此基础上，我们为MMDCDicL建立了一个可解释的网络结构。此外，在该结构中，我们设计了一个多核通道注意力模块（MKCAB），以高效地整合来自不同模态的信息。实验结果表明，MMDCDicL在定量和感知质量上都能重建出更高质量的结果。代码可在 此处下载。