Multi‐focus image fusion via morphological similarity‐based dictionary construction and sparse representation. Issue 2 (1st June 2018)

• Record Type: Journal Article
• Authors: Qi, Guanqiu; Zhang, Qiong; Zeng, Fancheng; Wang, Jinchuan; Zhu, Zhiqin
• Is Part Of: CAAI transactions on intelligence technology. Volume 3:Issue 2(2018)
• Page Start: 83
• Page End: 94
• Publication Date: 2018-06-01
• Subjects: iterative methods -- image classification -- image fusion -- dictionaries -- image representation; multifocus image fusion -- dictionary construction -- sparse representation -- informative dictionary -- sufficient bases -- dictionary learning -- different geometric information -- source images -- classified image bases -- constructed dictionary -- corresponding sparse coefficients -- Max‐L1 fusion rule -- final fused image -- state‐of‐the‐art fusion methods; B0290F Interpolation and function approximation (numerical analysis) -- B6135 Optical, image and video signal processing -- C1140Z Other topics in statistics -- C5260B Computer vision and image processing techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2018.0011
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : Sparse representation has been widely applied to multi‐focus image fusion in recent years. As a key step, the construction of an informative dictionary directly decides the performance of sparsity‐based image fusion. To obtain sufficient bases for dictionary learning, different geometric information of source images is extracted and analysed. The classified image bases are used to build corresponding subdictionaries by principle component analysis. All built subdictionaries are merged into one informative dictionary. Based on constructed dictionary, compressive sampling matched pursuit algorithm is used to extract corresponding sparse coefficients for the representation of source images. The obtained sparse coefficients are fused by Max‐L1 fusion rule first, and then inverted to form the final fused image. Multiple comparative experiments demonstrate that the proposed method is competitive with other the state‐of‐the‐art fusion methods.