Tensor completion using patch-wise high order Hankelization and randomized tensor ring initialization

Zheng, Jianwei; Qin, Mengjie; Xu, Honghui; Feng, Yuchao; Chen, Peijun; Chen, Shengyong

doi:10.1016/j.engappai.2021.104472

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…Note that the main code of our algorithm is provided in an encrypted format, which will be decrypted after the review phase. Four well-known quantitative metrics are selected to evaluate the performance of the SR task: root-mean-square error (RMSE), peak signalto-noise ratio (PSNR), spectral angle mapper (SAM), and structural similarity index (SSIM) [42].…”

Section: B Compared Approachesmentioning

confidence: 99%

Hyperspectral and Multispectral Data Fusion via Joint Local-Nonlocal Modeling and Truncation Operator

Jiang

Feng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fusion technology has been the core strategy to obtain a high-spatial-spectral resolution hyperspectral image (HSI). In recent years, few fusion models focused on exploiting the underlying manifold structure in the spatial dimension of the high-resolution HSI. We assume that image patches of highresolution multi-spectral image (HR-MSI) and high-resolution hyperspectral image (HR-HSI) share similar manifold structures. Through this bridge, a local-nonlocal low dimensional manifold defined from HR-MSI is built to favor the patch relationship in HR-HSI, which is further utilized to build a set of orthogonal bases. Subsequently, we introduce a truncation operation based on the adaptively constructed orthogonal bases, which can efficiently preserve the low-frequency information of the image and reduce the interference of noise. Finally, we combine the local-nonlocal manifold term and the truncation operation, coined as LNTM, into a variational super-resolution framework to regularize the latent HR-HSI, leading to a strongly convex function regarding the fusion problem. The cost function is solved by a carefully designed variant of alternating direction method of multipliers (ADMM) algorithm. Different experiments on three public benchmarks validate our algorithm outperforms the recent start-of-the-arts concerning both visual quality and numerical metrics.

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Section: B Compared Approachesmentioning

confidence: 99%