Structure-preserving image smoothing via contrastive learning

Dingkun ZHU, Weiming WANG, Xue XUE, Haoran XIE, Kwok Shing CHENG, Fu Lee WANG

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

Abstract

Image smoothing is an important processing operation that highlights low-frequency structural parts of an image and suppresses the noise and high-frequency textures. In the paper, we post an intriguing question of how to combine the paired unsmoothed/smoothed images and meaningful edge information to improve the performance of image smoothing. To this end, we propose a structure-preserving image smoothing network, which consists of a main interpreter (MI) and an edge map extractor (EME). The network is trained via contrastive learning on the extended BSD500 dataset. In addition, an edge-aware total variation loss function is utilized to distinguish between non-edge regions and edge maps via a pre-trained EME module, therefore improving the capability of structure preservation. In order to maintain the consistency in structure and background brightness, the outputs from MI are used as anchors for a ternary loss in 1:1 paired positive and negative samples. Experiments on different datasets show that our network outperforms state-of-the-art image smoothing methods in terms of SSIM and PSNR. Copyright © 2023 The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Original languageEnglish
JournalThe Visual Computer
Early online dateMay 2023
DOIs
Publication statusE-pub ahead of print - May 2023

Citation

Zhu, D., Wang, W., Xue, X., Xie, H., Cheng, G., & Wang, F. L. (2023). Structure-preserving image smoothing via contrastive learning. The Visual Computer. Advance online publication. https://doi.org/10.1007/s00371-023-02897-9

Keywords

  • Image smoothing
  • Structure preservation
  • Contrastive learning
  • Main interpreter
  • Edge map extractor

Fingerprint

Dive into the research topics of 'Structure-preserving image smoothing via contrastive learning'. Together they form a unique fingerprint.