With the development of deep learning technology, multi-spectral image super-resolution methods based on convolutional neural network have recently achieved great progress. However, the single hyperspectral image super-resolution remains a challenging problem due to the high-dimensional and complex spectral characteristics of hyperspectral data, which make it difficult to simultaneously capture spatial and spectral information. To deal with this issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the super-resolution of hyperspectral image. To be specific, we develop a new Feedback Structure and a Local-Global Spectral Block to alleviate the difficulty in spatial and spectral feature extraction. The Feedback Structure can transfer the high-level information to guide the generation process of low-level feature, which is achieved by a recurrent structure with finite unfoldings. Furthermore, in order to effectively use the high-level information passed back, a Local-Global Spectral Block is constructed to handle the feedback connections. The Local-Global Spectral Block utilizes the feedback high-level information to correct the low-level feature from local spectral bands and generates powerful high-level representations among global spectral bands. By incorporating the Feedback Structure and Local-Global Spectral Block, the FRLGN can fully exploit spatial-spectral correlations among spectral bands and gradually reconstruct high-resolution hyperspectral images. The source code of FRLGN is available at https://github.com/tangzhenjie/FRLGN.
翻译:随着深层学习技术的发展,基于共生神经网络的多光谱图像超分辨率方法最近取得了巨大进展,然而,单一超高光谱图像超分辨率仍是一个具有挑战性的问题,因为超光谱数据具有高度和复杂的光谱特性,因此难以同时捕捉空间和光谱信息;为了处理这一问题,我们提议为超光谱图像的超分辨率建立一个新的反馈改进地方-全球网络(FRLGN);具体而言,我们开发了新的反馈结构和一个地方-全球光谱屏,以减轻空间和光谱特征提取方面的困难。反馈结构可以转让高层次信息,指导低度特征生成过程,而低度特征生成过程是由一个经常结构随着有限的发展而实现的。此外,为了有效地使用过去的高层次信息,我们为处理超光谱图像的超光谱图像超级分辨率,我们建议建立一个地方-全球光谱区网络(FRGRGNB),利用反馈高层次信息来纠正当地光谱频谱波段的低度特征,并在全球光谱/光谱级图像中生成强大的高层次图像。通过一个经常性的BRBRBM-CRBRBM的系统,将GRBS-CFS-S-C-C-CRBAR的系统和GIS-CRBRBAR的系统和S-C-C-C-C-C-C-C-C-C-C-C-C-C-CRBAR-C-C-C-C-C-C-BAR-C-C-C-C-C-C-C-C-BAR-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-BAR-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C