Locally Linear Embedded Sparse Coding for Spectral Reconstruction From RGB Images

Training-based spectral reconstruction is an efficient, inexpensive technique to recover spectral images from the RGB images captured by trichromatic cameras. Existing methods handle training samples individually without any consideration of local spatial and spectral correlations between samples, which results in high metamerism and inaccurate reconstruction. In this letter, we exploit for the first time the concept of spectral image reconstruction from RGB images with both chromatic and texture priors. We reduce redundancy of the sample set by applying a volume maximization based selection strategy. Taking advantage of the local linearity and sparsity of spectra in dictionary learning, we propose a locally linear embedded sparse reconstruction method taking into account both RGB values of pixels and the features of patch texture. Experimental results show that our method is significantly more accurate than the state-of-the-art methods.