In this paper we are interested in exploiting self-similarity information for discriminative image denoising. Towards this goal, we propose a simple yet powerful denoising method based on transductive Gaussian processes, which introduces self-similarity in the prediction stage. Our approach allows to build a rich similarity measure by learning hyper parameters defining multi-kernel combinations. We introduce perceptual-driven kernels to capture pixel-wise, gradient-based and local-structure similarities. In addition, our algorithm can integrate several initial estimates as input features to boost performance even further. We demonstrate the effectiveness of our approach on several benchmarks. The experiments show that our proposed denoising algorithm has better performance than competing discriminative denoising methods, and achieves competitive result with respect to the state-of-the-art.