Infrared and Visible Image Fusion Based on Sparse Representation and Spatial Frequency in DTCWT Domain

Infrared and visible image fusion is a key area of research in multi-sensor image fusion. The main purpose of this fusion is to combine thermal information of the infrared image and texture information of the visible image. This paper presents an image fusion framework, based on parallel arrangement of sparse representation (SR) and spatial frequency (SF). In the proposed framework, an efficient edge-aware filter, i.e. guided filter, is first employed on the visible image. Then dual-tree complex wavelet transform (DTCWT) is used to obtain low-pass and high-pass coefficients of images, as it is shift-invariant and has high directional selectivity. The low-pass coefficients are fused using the SR- and SF-based fusion rules in parallel, which enhances the regional features of the images. The simulation results show that the proposed technique has better performance when compared with conventional techniques in both subjective and objective evaluations.