论文标题
图像超分辨率，具有深度变化自动编码器
Image Super-Resolution With Deep Variational Autoencoders
论文作者
论文摘要
图像超分辨率（SR）技术用于从低分辨率图像中生成高分辨率图像。到目前为止，已证明具有自回归模型和生成对抗网络（GAN）等深层生成模型已被证明有效地建模高分辨率图像。基于VAE的模型经常因其微弱的生成性能而受到批评，但是随着VDVAE等新的进步，现在有强有力的证据表明，深VAE具有优于当前最新模型的高分辨率图像产生。在本文中，我们引入了VDVAE-SR，这是一个新模型，旨在利用最新的Deep Vae方法来改善类似模型的结果。 VDVAE-SR使用经过验证的VDVAE的转移学习来解决图像超分辨率。提出的模型与其他最先进的模型具有竞争力，在图像质量指标上具有可比的结果。
Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models such as autoregressive models and Generative Adversarial Networks (GANs) have proven to be effective at modelling high-resolution images. VAE-based models have often been criticised for their feeble generative performance, but with new advancements such as VDVAE, there is now strong evidence that deep VAEs have the potential to outperform current state-of-the-art models for high-resolution image generation. In this paper, we introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE methodologies to improve upon the results of similar models. VDVAE-SR tackles image super-resolution using transfer learning on pretrained VDVAEs. The presented model is competitive with other state-of-the-art models, having comparable results on image quality metrics.
