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 language | English |
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Journal | The Visual Computer |
Early online date | May 2023 |
DOIs | |
Publication status | E-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/s00371-023-02897-9Keywords
- Image smoothing
- Structure preservation
- Contrastive learning
- Main interpreter
- Edge map extractor