Publications (11)
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
Jiangjun Peng, Yao Wang, Hongying Zhang +2
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local sm…
Beyond Low-rankness: Guaranteed Matrix Recovery via Modified Nuclear Norm
Jiangjun Peng, Yisi Luo, Xiangyong Cao +2
The nuclear norm (NN) has been widely explored in matrix recovery problems, such as Robust PCA and matrix completion, leveraging the inherent global low-rank structure of the data.…
Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation
Shuang Xu, Qiao Ke, Jiangjun Peng +2
This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed \textit{pan-denoising}. In a given scene, panchromatic (PAN) images capture similar…
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration
Shuang Xu, Chang Yu, Jiangjun Peng +2
Remote sensing image restoration aims to reconstruct missing or corrupted areas within images. To date, low-rank based models have garnered significant interest in this field. This…
Neural Gradient Regularizer
Shuang Xu, Yifan Wang, Zixiang Zhao +6
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one o…
Multi-Dimensional Visual Data Recovery: Scale-Aware Tensor Modeling and Accelerated Randomized Computation
Wenjin Qin, Hailin Wang, Jiangjun Peng +2
The recently proposed fully-connected tensor network (FCTN) decomposition has demonstrated significant advantages in correlation characterization and transpositional invariance, an…