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Hyperspectral Imaging is the acquisition of spectral and spatial informationof a particular scene. Capturing such information from a specializedhyperspectral camera remains costly. Reconstructing such information from theRGB image achieves a better solution in both classification and objectrecognition tasks. This work proposes a novel light weight network with veryless number of parameters about 233,059 parameters based on Residual densemodel with attention mechanism to obtain this solution. This network usesCoordination Convolutional Block to get the spatial information. The weightsfrom this block are shared by two independent feature extraction mechanisms,one by dense feature extraction and the other by the multiscale hierarchicalfeature extraction. Finally, the features from both the feature extractionmechanisms are globally fused to produce the 31 spectral bands. The network istrained with NTIRE 2020 challenge dataset and thus achieved 0.0457 MRAE metricvalue with less computational complexity.

Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images