A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing


  • Wei Liu The University of Adelaide
  • Pingping Zhang Dalian University of Technology
  • Yinjie Lei Sichuan University
  • Xiaolin Huang Shanghai Jiao Tong University
  • Jie Yang Shanghai Jiao Tong University
  • Ian Reid The University of Adelaide




Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications.




How to Cite

Liu, W., Zhang, P., Lei, Y., Huang, X., Yang, J., & Reid, I. (2020). A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 11620-11628. https://doi.org/10.1609/aaai.v34i07.6830



AAAI Technical Track: Vision