Paper ID | TEC-3.11 | ||
Paper Title | VISIBLE AND INFRARED IMAGE FUSION USING ENCODER-DECODER NETWORK | ||
Authors | Ferhat Can Ataman, Gözde Bozdaği Akar, Middle East Technical University, Turkey | ||
Session | TEC-3: Restoration and Enhancement 3 | ||
Location | Area G | ||
Session Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation | Poster | ||
Topic | Image and Video Processing: Restoration and enhancement | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | 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 https://github.com/ferhatcan/pyFusionSR. |