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Reconstruction of retinal spectra from RGB data using a RBF network

机译:使用RBF网络从RGB数据重建视网膜光谱

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In comparison with the standard three-channel colour images, spectral retinal images provide more detailed information about the structure of the retina. However, the availability of spectral retinal images for the research and development of image analysis methods is limited. In this paper, we propose two approaches to reconstruct spectral retinal images based on common RGB images. The approaches make use of fuzzy c-means clustering to perform quantization of the image data, and the radial basis function network to learn the mapping from the three-component color representation to the spectral space. The dissimilarities between the reconstructed spectral images and the original ones are evaluated on a retinal image set with spectral and RGB images, and by using a standard spectral quality metric. The experimental results show that the proposed approaches are able to reconstruct spectral retinal images with a relatively high accuracy.
机译:与标准的三通道彩色图像相比,光谱视网膜图像可提供有关视网膜结构的更多详细信息。但是,用于图像分析方法研究和开发的光谱视网膜图像的可用性是有限的。在本文中,我们提出了两种基于普通RGB图像重建光谱视网膜图像的方法。这些方法利用模糊c均值聚类对图像数据进行量化,并利用径向基函数网络学习从三分量颜色表示到光谱空间的映射。在重建的光谱图像和原始光谱图像之间的差异是在具有光谱和RGB图像的视网膜图像集上并通过使用标准光谱质量度量来评估的。实验结果表明,所提出的方法能够以较高的精度重建频谱视网膜图像。

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