论文标题
RGB图像的物理上合理的光谱重建
Physically Plausible Spectral Reconstruction from RGB Images
论文作者
论文摘要
最近，卷积神经网络（CNN）已用于从RGB图像中重建高光谱信息。此外，这种光谱重建问题（SR）通常可以通过良好（低）误差来解决。但是，这些方法在物理上并不合理：也就是说，恢复的光谱与潜在的摄像头敏感性重新整合时，所得的预测RGB与实际的RGB不同，有时这种差异可能很大。曝光变化进一步使问题更加复杂。实际上，大多数基于学习的SR模型都可以进行固定暴露设置，我们表明，当暴露量变化时，这可能会导致性能差。 在本文中，我们展示了如何扩展CNN学习，以便实现身体上的合理性，并减轻暴露量导致的问题。我们的SR解决方案在不同的暴露条件下改善了最新的光谱恢复性能，同时确保了物理上的合理性（恢复的光谱重新融合到输入RGB）。
Recently Convolutional Neural Networks (CNN) have been used to reconstruct hyperspectral information from RGB images. Moreover, this spectral reconstruction problem (SR) can often be solved with good (low) error. However, these methods are not physically plausible: that is when the recovered spectra are reintegrated with the underlying camera sensitivities, the resulting predicted RGB is not the same as the actual RGB, and sometimes this discrepancy can be large. The problem is further compounded by exposure change. Indeed, most learning-based SR models train for a fixed exposure setting and we show that this can result in poor performance when exposure varies. In this paper we show how CNN learning can be extended so that physical plausibility is enforced and the problem resulting from changing exposures is mitigated. Our SR solution improves the state-of-the-art spectral recovery performance under varying exposure conditions while simultaneously ensuring physical plausibility (the recovered spectra reintegrate to the input RGBs exactly).
