Multi-Focus Image Fusion using Self-Similarity and Depth Information in Nonsubsampled Shearlet Transform Domain

2016 ◽  
Vol 9 (1) ◽  
pp. 347-360 ◽  
Author(s):  
Liu Shuaiqi ◽  
Zhu Zhihui ◽  
Li Huiya ◽  
Zhao Jie ◽  
Wen Xin
Keyword(s):  
Image Fusion ◽  
Depth Information ◽  
Transform Domain ◽  
Self Similarity ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Focus Image

High quality multi-focus image fusion using self-similarity and depth information

2015 ◽  
Vol 338 ◽  
pp. 138-144 ◽  
Author(s):  
Di Guo ◽  
Jingwen Yan ◽  
Xiaobo Qu
Keyword(s):  
Image Fusion ◽  
Depth Information ◽  
Self Similarity ◽  
High Quality ◽  
Focus Image

Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain

2020 ◽  
Vol 31 (01) ◽  
pp. 2050050 ◽  
Author(s):  
Bo Li ◽  
Hong Peng ◽  
Xiaohui Luo ◽  
Jun Wang ◽  
Xiaoxiao Song ◽  
...  
Keyword(s):  
Image Fusion ◽  
Medical Images ◽  
Low Frequency ◽  
Transform Domain ◽  
Fusion Method ◽  
Shearlet Transform ◽  
Nonsubsampled Shearlet Transform ◽  
Model Combining ◽  
Fusion Methods ◽  
Image Fusion Method

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

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