Efficient regression priors for reducing image compression artifacts

Lossy image compression allows for large storage savings but at the cost of reduced fidelity of the compressed images. There is a fair amount of literature aiming at restoration by suppressing the compression artifacts. Very recently a learned semi-local Gaussian Processes-based solution (SLGP) has been proposed with impressive results. However, when applied to top compression schemes such as JPEG 2000, the improvement is less significant. In our paper we propose an efficient novel artifact reduction algorithm based on the adjusted anchored neighborhood regression (A+), a method from image super-resolution literature. We double the relative gains in PSNR when compared with the state-of-the-art methods such as SLGP, while being order(s) of magnitude faster.