Fusion of Multifucus Image with Noise Based on Adaptive Sparse and Low-Rank Representations

Traditional multifucus image fusion often requires the inclusion of edge features, blurred details, and noise pollution when perturbed by noise. To address these problems, this study proposes a method for fusing noisy multifucus images using adaptive sparse and low-rank representations. The proposed method first decomposes the image into high- and low-frequency subband coefficients using a non-subsampled shearlet transform. Subsequently, the high-frequency energy components are fused and denoised using a low-rank representation. The corresponding fusion rules are then set using an adaptive sparse representation to fuse the low-frequency subband coefficients. The final fusion result is obtained by reconstructing the fused high- and low-frequency subband coefficients. Experimental results show that the proposed method outperforms traditional methods in terms of both subjective performance and objective indicators, making it a compelling fusion method for noisy multifucus images.