  In this work we propose a novel postprocessing technique for
compression-artifact reduction. Our approach is based on posing this task as an
inverse problem, with a regularization that leverages on existing
state-of-the-art image denoising algorithms. We rely on the recently proposed
Plug-and-Play Prior framework, suggesting the solution of general inverse
problems via Alternating Direction Method of Multipliers (ADMM), leading to a
sequence of Gaussian denoising steps. A key feature in our scheme is a
linearization of the compression-decompression process, so as to get a
formulation that can be optimized. In addition, we supply a thorough analysis
of this linear approximation for several basic compression procedures. The
proposed method is suitable for diverse compression techniques that rely on
transform coding. Specifically, we demonstrate impressive gains in image
quality for several leading compression methods - JPEG, JPEG2000, and HEVC.
