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This research article presents a novel multimodal medical image fusion method that utilizes a parameter-adaptive pulse-coupled neural network (PAPCNN) and convolutional sparse representation (CSR) to enhance the quality of fused medical images. The proposed NSST-PAPCNN-CSR algorithm effectively addresses challenges in traditional fusion methods, such as poor detail preservation and difficult parameter settings, resulting in improved visual effects and objective performance metrics. Experimental results demo

Author

thurayasamir93

Language

EN