用卷积网络融合可见光与红外图像，实时且效果领先
Visible and Infrared Image Fusion Using Encoder-Decoder Network
- 仅用卷积和池化层构建编码解码结构
- 在多个数据集上优于当前最优方法，支持嵌入式设备实时运行
- 无需参考图像的损失函数，适合真实场景应用
多光谱图像融合旨在结合不同光谱特征图像中的目标或场景信息，以提升感知质量。本文提出一种基于学习的可见光与红外图像融合新方法，仅使用卷积和池化层，并采用基于无参考质量度量的损失函数。在多个数据集上进行了定性和定量分析，结果表明该方法性能优于现有先进方法。此外，网络规模小，可在嵌入式设备上实现实时处理。项目代码见：https://github.com/ferhatcan/pyFusionSR。
原文摘要 · Abstract (English)
The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than state-of-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at \url{https://github.com/ferhatcan/pyFusionSR}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。