A new MNF–BM4D denoising algorithm based on guided filtering for hyperspectral images. (September 2019)

• Record Type: Journal Article
• Authors: Xu, Ping; Chen, Bingqiang; Xue, Lingyun; Zhang, Jingcheng; Zhu, Lei; Duan, Hangbo
• Is Part Of: ISA transactions. Volume 92(2019)
• Page Start: 315
• Page End: 324
• Publication Date: 2019-09
• Subjects: Hyperspectral images -- BM4D -- MNF -- Guided filtering -- Denoising; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.02.018
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: This paper proposed a new MNF–BM4D denoising algorithm based on guided filtering to improve the denoising performance of the state-of-the-art Block-Matching and 4D filtering(BM4D) algorithm for hyperspectral images in the spatial and spectral domain. BM4D is firstly used to denoise hyperspectral images. Then Minimum Noise Fraction(MNF) algorithm is introduced to distinguish between the main component and the noisy component. Finally, the guided image filtering technology is utilized to further improve the denoising performance. A number of experiments on both simulated and real data are conducted to validate the effective denoising performance of the proposed method. Therefore, the proposed algorithm can be considered as a promising technique for hyperspectral imagery denoising. Highlights: A new MNF–BM4D denoising algorithm based on guided filtering is proposed. BM4D denoising performance is improved significantly in the spatial and spectral domain. Minimum Noise Fraction algorithm is introduced to distinguish different bands. Guided filtering technology is used to further improve the denoising performance.