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Non-local texture optimization with wasserstein regularization under convolutional neural network

journal contribution
posted on 2019-06-01, 00:00 authored by J Li, Yong XiangYong Xiang, Jingyu HouJingyu Hou, D Xu
Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an Expectation Maximization (EM)-like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures.

History

Journal

IEEE transactions on multimedia

Volume

21

Issue

6

Pagination

1437 - 1449

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1520-9210

Language

Eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018 IEEE