  Hyperspectral Imaging is the acquisition of spectral and spatial information
of a particular scene. Capturing such information from a specialized
hyperspectral camera remains costly. Reconstructing such information from the
RGB image achieves a better solution in both classification and object
recognition tasks. This work proposes a novel light weight network with very
less number of parameters about 233,059 parameters based on Residual dense
model with attention mechanism to obtain this solution. This network uses
Coordination Convolutional Block to get the spatial information. The weights
from this block are shared by two independent feature extraction mechanisms,
one by dense feature extraction and the other by the multiscale hierarchical
feature extraction. Finally, the features from both the feature extraction
mechanisms are globally fused to produce the 31 spectral bands. The network is
trained with NTIRE 2020 challenge dataset and thus achieved 0.0457 MRAE metric
value with less computational complexity.
