In this talk we present a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restored signals strengthened outcome. The convergence of this process is studied for the K-SVD image denoising and related algorithms. Furthermore, still in the context of K-SVD image denoising, we introduce an interesting interpretation of the SOS algorithm as a technique for closing the gap between the local patch-modeling and the global restoration task, thereby leading to improved performance. We demonstrate the SOS boosting algorithm for several leading denoising methods (KSVD, NLM, BM3D, and EPLL), showing tendency to further improve denoising performance.