高光谱成像
秩(图论)
图像(数学)
计算机科学
超分辨率
多光谱图像
分解
张量(固有定义)
财产(哲学)
分辨率(逻辑)
算法
人工智能
功能(生物学)
模式识别(心理学)
数学
化学
组合数学
纯数学
有机化学
哲学
认识论
生物
进化生物学
作者
X. Z. Cui,Jingya Chang
出处
期刊:Cornell University - arXiv
日期:2023-01-01
标识
DOI:10.48550/arxiv.2306.10489
摘要
Hyperspectral image (HSI) and multispectral image (MSI) fusion aims at producing a super-resolution image (SRI). In this paper, we establish a nonconvex optimization model for image fusion problems through low-rank tensor triple decomposition. Using the L-BFGS approach, we develop a first-order optimization algorithm for obtaining the desired super-resolution image (TTDSR). Furthermore, two detailed methods are provided for calculating the gradient of the objective function. With the aid of the Kurdyka-Lojasiewicz property, the iterative sequence is proved to converge to a stationary point. Finally, experimental results on different datasets show the effectiveness of our proposed approach.
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