In visual recognition and surveillance system, human face is one of the most important factors. Unfortunately, due to the low-cost imaging sensors and the complexity imaging environment, the captured face images are always low-resolution (LR) and corrupted by noise. The noisy LR face images possess limited useful information, which will extremely degrade the performance of face recognition system. To address this issue, in this paper we presented a K-nearest neighbor (K-NN) Regularized Collaborative Representation (K-RCR) method to simultaneously enhance the resolution of face images and suppress the noise. The proposed K-RCR breaks the bottlenecks of patch based face super-resolution methods, which makes it to be a reality that denoising and super-resolution can be achieved in a unified framework. Specifically, the K-NN selection strategy is employed to use the most important K nearest neighbors in the training dataset to collaboratively represent the test patch, leading to a unique and stable solution for the least squares problem. Moreover, a diagonal weight matrix is incorporated into the objective function to equip it more robust to noise. Experimental results on the standard test face dataset, i.e., FEI, demonstrate the superiority of our proposed method over several state-of-the-art face image super-resolution methods.