  Lossy compression introduces complex compression artifacts, particularly
blocking artifacts, ringing effects and blurring. Existing algorithms either
focus on removing blocking artifacts and produce blurred output, or restore
sharpened images that are accompanied with ringing effects. Inspired by the
success of deep convolutional networks (DCN) on superresolution, we formulate a
compact and efficient network for seamless attenuation of different compression
artifacts. To meet the speed requirement of real-world applications, we further
accelerate the proposed baseline model by layer decomposition and joint use of
large-stride convolutional and deconvolutional layers. This also leads to a
more general CNN framework that has a close relationship with the conventional
Multi-Layer Perceptron (MLP). Finally, the modified network achieves a speed up
of 7.5 times with almost no performance loss compared to the baseline model. We
also demonstrate that a deeper model can be effectively trained with features
learned in a shallow network. Following a similar "easy to hard" idea, we
systematically investigate three practical transfer settings and show the
effectiveness of transfer learning in low-level vision problems. Our method
shows superior performance than the state-of-the-art methods both on benchmark
datasets and a real-world use case.
