VISIBLE AND INFRARED IMAGE FUSION USING ENCODER-DECODER NETWORK

Ataman F. C., AKAR G.

2021 IEEE International Conference on Image Processing, ICIP 2021, Alaska, United States Of America, 19 - 22 September 2021, vol.2021-September, pp.1779-1783, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2021-September
  • Doi Number: 10.1109/icip42928.2021.9506740
  • City: Alaska
  • Country: United States Of America
  • Page Numbers: pp.1779-1783
  • Keywords: infrared, visible images, image fusion, deep learning, encoder-decoder network
  • Middle East Technical University Affiliated: Yes

Abstract

© 2021 IEEE.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.