Image Fusion Based on Compressed Sensing
Compressive sensing (CS) has received a lot of interest due to its compression capability and lack of complexity on the sensor side. This paper presented a new image fusion based on compressed sensing. The method decomposes two or more original images using directionlet transform, and gets the sparse matrix by the directionlet coefficients sparse representation, and fuses the sparse matrices with the coefficients absolute value maximum scheme. The compressed sample can be received through randomly observed. The fused image is recovered from the reduced samples by solving the optimization. The study demonstrates that CS-based image fusion has a number of perceived advantages in comparison with image fusion in the infrared image domain. The simulations show that the proposed CS-based image fusion algorithm has the advantages of simple structure and easy implementation, and also can achieve a better fusion performance.
KeywordsImage fusion Compressive sensing Directionlet transform
The authors are grateful to the anonymous referees for constructive comments. This study was funded by the Tianjin Normal University Doctoral Fund (52X09008).
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