<b>Spectral Super-Resolution Using CNN Decomposing a Color Image into Luminance and Chrominance Components</b>
<b>Spectral Super-Resolution Using CNN Decomposing a Color Image into Luminance and Chrominance Components</b>
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<b>使用 CNN 将彩色图像分解为亮度和色度分量的光谱超分辨率</b>
DOI：
10.11371/tievciieej.10.1_28
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Bisser Raytchev
中科院分区：
文献类型：
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作者：
Masahiro Sakamoto;Kazufumi Kaneda;Bisser Raytchev
< Summary>高光谱图像在工业、医学、遥感等领域有着广泛的应用，在计算机图形学中也被用作光谱绘制中的光探针图像和纹理。然而，获取光谱图像在设备和时间方面是昂贵的，这阻碍了其获取和使用。使用深度学习的传统光谱超分辨率一直采用直接的端到端学习方法来处理RGB和高光谱图像。相比之下，我们关注的是高光谱图像被分解为亮度和色度分量，我们提出了一种新的光谱超分辨率，使用深度学习来分别估计每个分量。最后，在所提出的方法中，高光谱图像重建相结合的亮度和色度分量的估计。
< Summary> Hyper-spectral images are used in a wide range of fields such as industry, medicine, remote sensing, and so on. They are also used in computer graphics as light probe images and textures in spectral rendering. The acquisition of spectral images is, however, costly in terms of equipment and time, which hinders its acquisition and use. Conventional spectral super-resolutions using deep learning have been adopting a direct end-to-end learning method to RGB and hyper-spectral images. In contrast, we focus on the fact that hyper-spectral images are decomposed into luminance and chrominance components, and we propose a novel spectral super-resolution using a deep learning to estimate each component separately. Finally, in the proposed method, a hyper-spectral image is reconstructed by combining the estimated luminance and chrominance components.