Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm

Lu，Jingjing1; Zhang，Jun1,2; Wang，Chao3,4; Deng，Chengzhi1

2024-07-01

10.1016/j.sigpro.2024.109449

Signal Processing   影响因子和分区

0165-1684

Many recent studies have shown that the adaptive total variation regularization has the advantage of better preserving local features of images compared with the celebrated total variation regularization. On the other hand, the superpixel-based weighted nuclear norm can compensate for the shortcomings of the superpixel-based standard nuclear norm, assigning different weights to singular values and improving flexibility. Inspired by these two factors, we propose two new hyperspectral sparse fusion models related to the adaptive total variation regularization and superpixel-based weighted nuclear norm. Furthermore, we design the alternating direction method of multipliers (ADMM) to efficiently solve the proposed models, with complexity and convergence analyses. Experimental results demonstrate that the proposed methods outperform several state-of-the-art methods both numerically and visually.

Adaptive total variation ADMM Hyperspectral image fusion Sparsity Weighted nuclear norm

SCI ; EI

英语

其他

20241015698714

Image fusion

Data Processing and Image Processing:723.2

ENGINEERING

2-s2.0-85186651728

Scopus

1.Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing,Nanchang Institute of Technology,Jiangxi,Nanchang,330099,China
2.College of Science,Nanchang Institute of Technology,Jiangxi,Nanchang,330099,China
3.Department of Statistics and Data Science,Southern University of Science and Technology,Guangdong,Shenzhen,518055,China
4.National Centre for Applied Mathematics Shenzhen,Guangdong Province,Shenzhen,518055,China

Lu，Jingjing,Zhang，Jun,Wang，Chao,et al. Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm[J]. Signal Processing,2024,220.

Lu，Jingjing,Zhang，Jun,Wang，Chao,&Deng，Chengzhi.(2024).Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm.Signal Processing,220.

Lu，Jingjing,et al."Hyperspectral sparse fusion using adaptive total variation regularization and superpixel-based weighted nuclear norm".Signal Processing 220(2024).