The spatial details injection model has been considered as a general framework in the literature of pansharpening , and recently there have been significant advances in this framework based on sparse representation (SR) of spatial details. However, the SR-based methods have greater computational burden in estimating the sparse vectors and limited ability in detail edge preservation. In this article, we introduce the neighbor embedding (NE) instead of SR-based model and the edge-preserving filter into the spatial detail injection framework to address the aforementioned two drawbacks. By utilizing the best quality of NE, we propose the detail injection via NE (DINE) algorithm for pansharpening, and DINE+, an improved variant of DINE by using the edge-preserving filter to enhance the spatial details. Experiments carried on three datasets captured by different satellite sensors and compared with current state-of-the-art methods validate the effectiveness of the proposed methods.

中文翻译：

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通过邻居嵌入空间细节进行泛锐化

这 空间细节 注射模型已被视为文献中的一般框架 锐化 ，并且最近基于 稀疏表示（SR）的空间细节。但是，基于SR的方法在估计稀疏矢量时具有较大的计算负担，并且在细节边缘保留方面的能力有限。在本文中，我们介绍了邻居嵌入（NE）代替基于SR的模型和将保留边缘的滤波器放入空间细节注入框架中，以解决上述两个缺点。通过利用NE的最佳质量，我们建议通过NE进行细节注入（DINE）泛锐化算法，以及DINE +，这是DINE的改进变体，通过使用边缘保留滤镜增强了空间细节。对由不同卫星传感器捕获的三个数据集进行的实验，并与当前的最新方法进行了比较，验证了所提出方法的有效性。