  Capturing visual image with a hyperspectral camera has been successfully
applied to many areas due to its narrow-band imaging technology. Hyperspectral
reconstruction from RGB images denotes a reverse process of hyperspectral
imaging by discovering an inverse response function. Current works mainly map
RGB images directly to corresponding spectrum but do not consider context
information explicitly. Moreover, the use of encoder-decoder pair in current
algorithms leads to loss of information. To address these problems, we propose
a 4-level Hierarchical Regression Network (HRNet) with PixelShuffle layer as
inter-level interaction. Furthermore, we adopt a residual dense block to remove
artifacts of real world RGB images and a residual global block to build
attention mechanism for enlarging perceptive field. We evaluate proposed HRNet
with other architectures and techniques by participating in NTIRE 2020
Challenge on Spectral Reconstruction from RGB Images. The HRNet is the winning
method of track 2 - real world images and ranks 3rd on track 1 - clean images.
Please visit the project web page
https://github.com/zhaoyuzhi/Hierarchical-Regression-Network-for-Spectral-Reconstruction-from-RGB-Images
to try our codes and pre-trained models.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning