Frequency Domain

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  1. Distinguish generated fake images and real images in the freqency domain. [2]

  2. Use frequency map as network input or output [1] [5] [6]

  3. Use intermediate frequency features [7] [9]

  4. An image can be composed of or decomposed into low-frequency part and high-frequency part [3] [8] [4] [10]

Reference

  1. Kai Xu, Minghai Qin, Fei Sun, Yuhao Wang, Yen-Kuang Chen, Fengbo Ren, “Learning in the Frequency Domain”, CVPR, 2020.

  2. Wang, Sheng-Yu, et al. “CNN-generated images are surprisingly easy to spot… for now.” arXiv preprint arXiv:1912.11035 (2019).

  3. ayush Bansal, Yaser Sheikh, Deva Ramanan, “PixelNN: Example-based Image Synthesis”, ICLR 2018.

  4. Yanchao Yang, Stefano Soatto, “FDA: Fourier Domain Adaptation for Semantic Segmentation”, CVPR 2020.

  5. Roy, Hiya, et al. “Image inpainting using frequency domain priors.” arXiv preprint arXiv:2012.01832 (2020).

  6. Shen, Xing, et al. “DCT-Mask: Discrete Cosine Transform Mask Representation for Instance Segmentation.” arXiv preprint arXiv:2011.09876 (2020).

  7. Suvorov, Roman, et al. “Resolution-robust Large Mask Inpainting with Fourier Convolutions.” WACV (2021).

  8. Yu, Yingchen, et al. “WaveFill: A Wavelet-based Generation Network for Image Inpainting.” ICCV, 2021.

  9. Mardani, Morteza, et al. “Neural ffts for universal texture image synthesis.” NeurIPS (2020).

  10. Cai, Mu, et al. “Frequency domain image translation: More photo-realistic, better identity-preserving.” ICCV, 2021.