TY - JOUR
T1 - Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising
AU - Tu , Zhihui
AU - Chen , Shunda
AU - Lu , Jian
AU - Li , Lin
AU - Jiang , Qingtang
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 2
SP - 379
EP - 403
PY - 2024
DA - 2024/05
SN - 17
DO - http://doi.org/10.4208/nmtma.OA-2023-0135
UR - https://global-sci.org/intro/article_detail/nmtma/23105.html
KW - Remote sensing images, denoising, tensor decomposition, non-local self-similarity.
AB - <p style="text-align: justify;">Remote sensing images (RSIs) encompass abundant spatial and spectral/temporal information, finding wide applications in various domains. However,
during image acquisition and transmission, RSI often encounter noise interference,
which adversely affects the accuracy of subsequent applications. To address this issue, this paper proposes a novel non-local fully connected tensor network (NLFCTN)
decomposition algorithm for denoising RSI, aiming to fully exploit their global correlation and non-local self-similarity (NSS) characteristics. FCTN, as a recently developed tensor decomposition technique, exhibits remarkable capability in capturing global correlations and minimizing information loss. In addition, we introduce
an efficient algorithm based on proximal alternating minimization (PAM) to efficiently solve the model and prove the convergence. The effectiveness of the proposed method is validated through denoising experiments on both simulated and
real RSI data, employing objective evaluation metrics and subjective visual assessments. The results of the experiment show that the proposed method outperforms
other RSI denoising techniques in terms of denoising performance.</p>