Summary of Rethinking Normalization Strategies and Convolutional Kernels For Multimodal Image Fusion, by Dan He et al.
Rethinking Normalization Strategies and Convolutional Kernels for Multimodal Image Fusion
by Dan He, Guofen Wang, Weisheng Li, Yucheng Shu, Wenbo Li, Lijian Yang, Yuping Huang, Feiyan Li
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multimodal image fusion (MMIF) method is proposed to integrate information from different modalities, enhancing downstream tasks. Existing methods prioritize natural image fusion and ignore the distinct requirements of medical image fusion. The paper highlights the differences in fusion goals, statistical properties, and data distribution between natural and medical MMIF. A novel mixture of instance normalization and group normalization is introduced to preserve sample independence and reinforce intrinsic feature correlation. Additionally, a large kernel convolution and multipath adaptive fusion module are proposed to enhance feature maps and transmission of crucial information. The method achieves state-of-the-art performance in multiple fusion tasks and improves downstream applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MMIF aims to combine different image types for better results. Current methods focus on natural images and don’t consider medical images, which require different approaches. This paper shows that medical images need a unique way of combining information from various sources. A new method is introduced that combines two normalizing techniques and uses larger filters to keep important details. The method works well in multiple situations and improves results when used with other tasks. |
