Back to articles
Volume: 31 | Article ID: art00011
CNN Based Parameter Optimization for Texture Synthesis
  DOI :  10.2352/ISSN.2470-1173.2019.6.MAAP-484  Published OnlineJanuary 2019

Texture synthesis is the process of generating a large texture image from a small texture sample. The synthesized image must appear as though it has the same underlying structural content as the input texture sample. However, most texture synthesis methods require the user to tune parameters for different input or provide feedback to the system to achieve satisfactory results. To make texture synthesis approaches more efficient and user friendly, we propose a fully automatic method to select a set of suitable parameters for texture synthesis that can be applied on commonly used textures. Our method uses Convolutional Neural Network (CNN) to predict the optimal parameters for texture synthesis based on image quilting algorithm . Our method showed satisfactory results on different types of textures.

Subject Areas :
Views 14
Downloads 2
 articleview.views 14
 articleview.downloads 2
  Cite this article 

Jiangpeng He, Kyle Ziga, Judy Bagchi, Fengqing Zhu, "CNN Based Parameter Optimization for Texture Synthesisin Proc. IS&T Int’l. Symp. on Electronic Imaging: Material Appearance,  2019,  pp 484-1 - 484-6,

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2019
Electronic Imaging
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA