Image Sequence Fusion and Denoising Based on 3D Shearlet Transform

Abstract

We propose a novel algorithm for image sequence fusion and denoising simultaneously in 3D shearlet transform domain. In general, the most existing image fusion methods only consider combining the important information of source images and do not deal with the artifacts. If source images contain noises, the noises may be also transferred into the fusion image together with useful pixels. In 3D shearlet transform domain, we propose that the recursive filter is first performed on the high-pass subbands to obtain the denoised high-pass coefficients. The high-pass subbands are then combined to employ the fusion rule of the selecting maximum based on 3D pulse coupled neural network (PCNN), and the low-pass subband is fused to use the fusion rule of the weighted sum. Experimental results demonstrate that the proposed algorithm yields the encouraging effects.

Review of Image Fusion Based on Pulse-Coupled Neural Network

“…Thee pyramidal regions are obtained by partition of the Fourier space. In order to get better performance, they propose a spatial-temporal fusion rule based on 3D PCNN [72]. Geng et al [73,74] and Ma et al [75] also employ PCNN to fuse image in ST domain.…”

Infrared‐visible video fusion based on motion‐compensated wavelet transforms

“…Besides 2D transform methods, 3D extension versions of 2D transforms have also been built (e.g. 3D DWT, 3D dual-tree complex wavelet transform (DT-CWT) [43], 3D surfacelet [44] and 3D shearlet [45,46]). …”