Continuous image super-resolution (SR) recently receives a lot of attention from researchers, for its practical and flexible image scaling for various displays. Local implicit image representation is one of the methods that can map the coordinates and 2D features for latent space interpolation. Inspired by Variational AutoEncoder, we propose a Soft-introVAE for continuous latent space image super-resolution (SVAE-SR). A novel latent space adversarial training is achieved for photo-realistic image restoration. To further improve the quality, a positional encoding scheme is used to extend the original pixel coordinates by aggregating frequency information over the pixel areas. We show the effectiveness of the proposed SVAE-SR through quantitative and qualitative comparisons, and further, illustrate its generalization in denoising and real-image super-resolution.

翻译：连续图像超分辨率（SR）因其在各类显示设备中实现实用且灵活的图像缩放而近期备受研究者关注。局部隐式图像表示是一种能够将坐标与二维特征映射用于潜在空间插值的方法。受变分自编码器的启发，我们提出了一种用于连续潜在空间图像超分辨率的Soft-IntroVAE（SVAE-SR）。通过创新性的潜在空间对抗训练，实现了照片级真实感的图像复原。为进一步提升质量，采用位置编码方案通过聚合像素区域的频率信息来扩展原始像素坐标。我们通过定量与定性比较验证了所提出的SVAE-SR的有效性，并进一步展示了其在去噪及真实图像超分辨率任务中的泛化能力。