TY - JOUR
AU - Akar, Gözde Bozdaği
AB - 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 \url{this https URL}.
TI - Visible and Infrared Image Fusion Using Encoder-Decoder Network
JF - Electrical Engineering and Systems Science
DO - 10.1109/icip42928.2021.9506740
DA - 2024-12-11
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/visible-and-infrared-image-fusion-using-encoder-decoder-network-8w90FXZlti
VL - 2024
IS - 2412
DP - DeepDyve
ER -