Compressive sensing image fusion in heterogeneous sensor networks based on shearlet and wavelet transform

Abstract

Heterogeneous image fusion is a technique of fusing images captured by different sensors into one image, then the fused image will present more information than the original images. This paper studies the compressive sensing image fusion algorithm and applies shearlet and wavelet transforms to represent the image sparsely. By compressing the sampled coefficients of the original images, the computational complexity in the image fusion process is reduced and the fusion efficiency is improved. We focus on the image fusion rules of compressive domain. Image coefficients of different frequencies are compressed by various sampling rates and fused according to different fusion rules. So an ideal fusion results can be obtained under a low sampling rate.

Visual sensor image enhancement based on non-sub-sampled shearlet transform and phase stretch transform

“…It has good results in image processing applications, such as image fusion, enhancement, and denoising. In our early research, we have applied non-sub-sampled shearlet transform (NSST) to enhance images adaptively and fuse images in compressive domain [10,11].…”