Parametric meta-filter modeling from a single example pair

Shi Sheng Huang, Guo Xin Zhang, Yu Kun Lai, Johannes Kopf, Daniel Cohen-Or, Shi Min Hu

Research output: Contribution to journalArticlepeer-review


We present a method for learning a meta-filter from an example pair comprising an original image A and its filtered version A using an unknown image filter. A meta-filter is a parametric model, consisting of a spatially varying linear combination of simple basis filters. We introduce a technique for learning the parameters of the meta-filter f such that it approximates the effects of the unknown filter, i.e.; f (A) approximates A . The meta-filter can be transferred to novel input images, and its parametric representation enables intuitive tuning of its parameters to achieve controlled variations. We show that our technique successfully learns and models meta-filters that approximate a large variety of common image filters with high accuracy both visually and quantitatively.

Original languageEnglish
Pages (from-to)673-684
Number of pages12
JournalVisual Computer
Issue number6-8
StatePublished - Jun 2014


  • Filter space
  • Image filters
  • Learning and Transfer
  • Sparsity

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Parametric meta-filter modeling from a single example pair'. Together they form a unique fingerprint.

Cite this