Recently developed generative methods, including invertible rescaling network
(IRN) based and generative adversarial network (GAN) based methods, have
demonstrated exceptional performance in image rescaling. However, IRN-based
methods tend to produce over-smoothed results, while GAN-based methods easily
generate fake details, which thus hinders their real applications. To address
this issue, we propose Boundary-aware Decoupled Flow Networks (BDFlow) to
generate realistic and visually pleasing results. Unlike previous methods that
model high-frequency information as standard Gaussian distribution directly,
our BDFlow first decouples the high-frequency information into \textit{semantic
high-frequency} that adheres to a Boundary distribution and
\textit{non-semantic high-frequency} counterpart that adheres to a Gaussian
distribution. Specifically, to capture semantic high-frequency parts
accurately, we use Boundary-aware Mask (BAM) to constrain the model to produce
rich textures, while non-semantic high-frequency part is randomly sampled from
a Gaussian distribution.Comprehensive experiments demonstrate that our BDFlow
significantly outperforms other state-of-the-art methods while maintaining
lower complexity. Notably, our BDFlow improves the PSNR by $4.4$ dB and the
SSIM by $0.1$ on average over GRAIN, utilizing only 74\% of the parameters and
20\% of the computation. The code will be available at
this https URL

提出了 Boundary-aware Decoupled Flow Networks (BDFlow) 来生成逼真且视觉上令人愉悦的结果，通过将高频信息分解为 “语义高频” 和 “非语义高频” 两部分，并使用 Boundary-aware Mask (BAM) 约束模型生成丰富的纹理，实验证明 BDFlow 在保持较低复杂性的同时，显著优于其他最先进的方法。