Open AccessInfrared and visible image fusion based on non‐subsampled shearlet transform, regional energy, and co‐occurrence filtering
Author(s) -
Zhang Shuang,
Liu Feng
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.0557
Subject(s) - image fusion , contrast (vision) , artificial intelligence , fusion , shearlet , computer vision , infrared , energy (signal processing) , image (mathematics) , computer science , fusion rules , pattern recognition (psychology) , top hat transform , enhanced data rates for gsm evolution , image processing , feature detection (computer vision) , mathematics , optics , physics , statistics , philosophy , linguistics
The fusion of infrared and visible images has been playing an important role in various scenarios all over the world. For the fusion results from most of the existing techniques in this area, some features, such as the image contrast and edge details, are still needed to be improved. In this Letter, a new fusion method of the infrared and visible image is rendered. In this method, the infrared image is preprocessed to improve the contrast. Then, the two source images are decomposed based on non‐subsampled shearlet transform (NSST). The fusion rules based on regional energy and co‐occurrence filtering are proposed for the low‐frequency and high‐frequency NSST coefficients, respectively. Experimental results show that the proposed method can effectively retain the details of the source image, meanwhile improve the contrast of the fused image.