1. Morphological Component Analysis-Based Perceptual Medical Image Fusion Using Convolutional Sparsity-Motivated PCNN
- Author
-
Kaili Liu, Xiao Li, Chuangeng Tian, Jian Wang, and Lu Tang
- Subjects
Image fusion ,Fusion ,Artificial neural network ,Article Subject ,Computer science ,business.industry ,media_common.quotation_subject ,020206 networking & telecommunications ,Pattern recognition ,02 engineering and technology ,Sparse approximation ,Texture (music) ,Computer Science Applications ,Image (mathematics) ,Stimulus (psychology) ,QA76.75-76.765 ,Perception ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Computer software ,business ,Software ,media_common - Abstract
This paper proposes a perceptual medical image fusion framework based on morphological component analysis combining convolutional sparsity and pulse-coupled neural network, which is called MCA-CS-PCNN for short. Source images are first decomposed into cartoon components and texture components by morphological component analysis, and a convolutional sparse representation of cartoon layers and texture layers is produced by prelearned dictionaries. Then, convolutional sparsity is used as a stimulus to motivate the PCNN for dealing with cartoon layers and texture layers. Finally, the medical fused image is computed via combining fused cartoon layers and texture layers. Experimental results verify that the MCA-CS-PCNN model is superior to the state-of-the-art fusion strategy.
- Published
- 2021