Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects. Inspired by the deep convolutional networks (DCN) on super-resolution [5] , we formulate a compact and efficient network for seamless attenuation of different compression artifacts. We also demonstrate that a deeper model can be effectively trained with the features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate several 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-arts both on the benchmark datasets and the real-world use case (i.e. Twitter).
According to REF , ARCNN shows superior performance compared with the state-of-the-art conventional methods.
Compression Artifacts Reduction by a Deep Convolutional Network
2015 IEEE International Conference on Computer Vision (ICCV)
2015 IEEE International Conference on Computer Vision (ICCV)
Computer Science