LAUSR

Spectral reflectance reconstruction by the local linear model has many advantages such as simple model structure, fast calculation speed and less chance to be over-fitting. However, local linear model often suffers from the outliers and prone to be under-fitting. In this paper, we propose a locally weighted linear model for spectral reflectance reconstruction. It improves the contribution of the local neighbors with higher similarity to the test point, which can reduce the influence of noisy points and redundant points. Experimental results show that the locally weighted linear model can effectively reduce the reconstruction error.