Visible and Infrared Image Fusion Using Encoder-Decoder Network
This work addresses image fusion for multispectral applications, but it appears incremental as it builds on existing encoder-decoder networks with a focus on efficiency.
The authors tackled the problem of fusing visible and infrared images to enhance perceptual quality, achieving better performance than state-of-the-art methods and enabling real-time processing on embedded devices.
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}.