  In this paper we propose a generic recursive algorithm for improving image
denoising methods. Given the initial denoised image, we suggest repeating the
following "SOS" procedure: (i) (S)trengthen the signal by adding the previous
denoised image to the degraded input image, (ii) (O)perate the denoising method
on the strengthened image, and (iii) (S)ubtract the previous denoised image
from the restored signal-strengthened outcome. The convergence of this process
is studied for the K-SVD image denoising and related algorithms. 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. In a quest for the theoretical origin of the SOS algorithm, we
provide a graph-based interpretation of our method, where the SOS recursive
update effectively minimizes a penalty function that aims to denoise the image,
while being regularized by the graph Laplacian. We demonstrate the SOS boosting
algorithm for several leading denoising methods (K-SVD, NLM, BM3D, and EPLL),
showing tendency to further improve denoising performance.
