Retinex decomposition with alternating direction minimization techniques is a prevalent solution for low light enhancement. This is because estimating the reflectance and illumination is typically considered as a multi block convex optimization problem,resulting in complex algorithm structure and low optimization efficiency. In this paper,we propose a regularized Gaussian fields(RGF)model for low light enhancement. The RGF based optimization function is established to formulate simultaneous reflectance and illumination estimation as an unconstrained optimization problem. This function is differentiable and hence could be minimized by standard gradient based optimization techniques. Then,the reflectance solved from the RGF based optimization function is refined based on the Gaussian kernel to avoid over exposure and preserve naturalness. The qualitative and quantitative comparisons on challenging low light images demonstrate the superiority of our method over several state of the arts in terms of enhancement efficiency and quality.