Abstract
In this paper, a new method of combination single layer wavelet transform and compressive sensing is proposed for image fusion. In which only measured the high-pass wavelet coefficients of the image but preserved the low-pass wavelet coefficient. Then, fuse the low-pass wavelet coefficients and the measurements of high-pass wavelet coefficient with different schemes. For the reconstruction, by using the minimization of total variation algorithm (TV), high-pass wavelet coefficients could be recovered by the fused measurements. Finally, the fused image could be reconstructed by the inverse wavelet transform. The experiments show the proposed method provides promising fusion performance with a low computational complexity.
Highlights
Image fusion is the technique that integrates complementary and redundant information of multiple images to obtain a composite one, which contains more comprehensive description than any of the individual image
We proposed an image fusion scheme in a general compressive sensing (CS) framework
Three groups of test images are employed for the performance evaluation to illustrate the effectiveness of the proposed approach
Summary
Image fusion is the technique that integrates complementary and redundant information of multiple images to obtain a composite one, which contains more comprehensive description than any of the individual image. A new technique for simultaneous data sampling and compression known as compressive sensing (CS) [4] [5] [6] [7] has been developed. It builds upon the ground breaking work by Donoho [7] and Candes et al [8], who showed that under certain conditions, a signal can be precisely recovered from only a small set of measurements. We proposed an image fusion scheme in a general CS framework.
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