Multi-Sensor Image Fusion Based on Interval Type-2 Fuzzy Sets and Regional Features in Nonsubsampled Shearlet Transform Domain

Abstract

Multi-scale geometric analysis, one of the most often-used multi-sensor image fusion (MSIF) techniques, can offer outstanding performance during extracting the features of source image. Interval type-2 fuzzy sets (Type-2 FS) have a good prospect in image fusion field, because it can effectively address the uncertain and fuzzy problem in image fusion for selecting the high-quality pixels or coefficients of source images. We try to extend the application fields of Type-2 FS and improve the performance of MSIF; therefore, this paper presents a hybrid method by combining the local spatial frequency (LSF) with interval Type-2 FS in nonsubsampled shearlet transform (NSST) domain. NSST is used to decompose source images, and interval Type-2 FS and LSF is employed to extract the regional features of source images; so it can extract and fuse the detailed features of different source images accurately. First, NSST is performed to decompose the source images into low frequency and high frequency sub-images. Second, LSF-based fusion rule is applied to fuse low frequency sub-images. Thirdly, a novel fusion process based on interval Type-2 FS is designed to fuse high frequency sub-images. At last, inverse NSST2 (INSST) is implemented to reconstruct the fused images. The experimental and contrastive results of different image sets show that the proposed method is an effective MSIF scheme, which can achieve better fusion effect than the existing representative methods.