Visible and near-infrared image fusion based on information complementarity

Abstract

Images with complementary spectral information can be recorded using image sensors that can identify visible and near-infrared spectrum. The fusion of visible and near-infrared (NIR) aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data processing. Unfortunately, current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information complementarity. Therefore, an information complementarity fusion (ICF) model is designed based on physical signals. In order to separate high-frequency noise from important information in distinct frequency layers, the authors first extracted texture-scale and edge-scale layers using a two-scale filter. Second, the difference map between visible and near-infrared was filtered using the extended-DoG filter to produce the initial visible-NIR complementary weight map. Then, to generate a guide map, the near-infrared image with night adjustment was processed as well. The final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight maps. Finally, fusion images were generated with the complementarity weight maps. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art in both avoiding artificial colours as well as effectively utilising information complementarity.