Color demosaicing is a key image processing step aiming to reconstruct the missing pixels from a recorded raw image. On the one hand, numerous interpolation methods focusing on spatial-spectral correlations have been proved very efficient, whereas they yield a poor image quality and strong visible artifacts. On the other hand, optimization strategies such as learned simultaneous sparse coding (LSSC) and sparsity and adaptive PCA (SAPCA) based algorithms were shown to greatly improve image quality compared to that delivered by interpolation methods, but unfortunately are computationally heavy. In this paper we propose âefficient regression priors (ERP)â as a novel, fast post-processing algorithm that learns the regression priors offline from training data. We also propose an independent efficient demosaicing algorithm based on directional difference regression (DDR), and introduce its enhanced version based on fused regression (FR). We achieve an image quality comparable to that of state-of-the-art methods for three benchmarks, while being order(s) of magnitude faster.