HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images

Zhan Shi, Chang Chen, Zhiwei Xiong, Dong Liu, Feng Wu
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Hyperspectral recovery from a single RGB image has seen a great improvement with the development of deep convolutional neural networks (CNNs). In this paper, we propose two advanced CNNs for the hyperspectral reconstruction task, collectively called HSCNN+. We first develop a deep residual network named HSCNN-R, which comprises a number of residual blocks. The superior performance of this model comes from the modern architecture and optimization by removing the hand-crafted upsampling in HSCNN.
more » ... Based on the promising results of HSCNN-R, we propose another distinct architecture that replaces the residual block by the dense block with a novel fusion scheme, leading to a new network named HSCNN-D. This model substantially deepens the network structure for a more accurate solution. Experimental results demonstrate that our proposed models significantly
doi:10.1109/cvprw.2018.00139 dblp:conf/cvpr/ShiCXLW18 fatcat:bu3yujyiyvd3dhlddkjkbzrcxi