Abstract:

Since the compressive sampling of Block-Based Compressed Sensing (BCS) in spatial domain lacks of considering the global features of an image, image fusion based on conventional BCS sampling suffers from reduced quality and blocking artifacts during reconstruction. Firstly, the input images were sparsely represented by Contourlet Transform (CT), then the Contourlet Transform Block-Based Compressed Sensing (CTBCS) sampling was implemented in the CT domain. Secondly, the compressive samplings were fused by the rule of linear weighting. Finally, the fused image was reconstructed by Iterative Thresholding Projection (ITP) algorithm with consideration of blocking artifacts. The fusion method based on CTBCS was proposed for remote-sensing images, and the implementation algorithm was also presented in detail. In the simulation experiments, BCS and CTBCS were used for compressive sampling, then ITP algorithm was used for image reconstruction. The simulation results show that, compared with BCS, CTBCS sampling which considered the global characteristics has higher convergence speed, less computational complexity and higher reconstructing accuracy, the corresponding Peak Signal-to-Noise Ratio (PSNR) of recovery image is also higher. The real data tests indicate that the compressive fusion based on CTBCS achieves better result than that based on BCS. With very small amount of samples, the CTBCS-based compressive fusion can achieve a comparable result with fusion by the conventional CT method. Therefore, the proposed fusion method effectively implements the compressive fusion for the remote-sensing images with large amounts of data.