Visible and Infrared Image Fusion Using Encoder-Decoder Network

Autor:	Ataman, Ferhat Can, Akar, Gözde Bozdaği
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning Electrical Engineering and Systems Science - Image and Video Processing
Zdroj:	2021 IEEE International Conference on Image Processing (ICIP), pages 1779-1783, Publication date: 2021/9/19
Druh dokumentu:	Working Paper
DOI:	10.1109/ICIP42928.2021.9506740
Popis:	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}. Comment: 5 pages, published at ICIP 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.08073 Zobrazit plný text záznamu View this record from Arxiv