论文标题
来自RGB图像的光谱重建的分层回归网络
Hierarchical Regression Network for Spectral Reconstruction from RGB Images
论文作者
论文摘要
通过高光谱摄像头捕获视觉图像，由于带狭窄的带成像技术，已成功应用于许多区域。来自RGB图像的高光谱重建是通过发现反响应函数的高光谱成像的反向过程。当前的作品主要将RGB图像直接映射到相应的频谱，但没有明确考虑上下文信息。此外，在当前算法中使用编码器对会导致信息丢失。为了解决这些问题，我们提出了一个4级层次回归网络（HRNET），其像素交换层是层间相互作用。此外，我们采用残留的密集块来消除现实世界RGB图像的工件和一个残留的全球块，以建立注意感知领域的注意机制。我们通过参与RGB图像的光谱重建挑战，通过参与NTIRE 2020挑战来评估提出的HRNET。 HRNET是轨道2的获胜方法 - 现实世界图像，在轨道1上排名第三-清洁图像。请访问项目网页https://github.com/zhaoyuzhi/hierarchical-regression-network-for-spectral-reconstruction-from-rgb-images来尝试我们的代码和预培训模型。
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.
