Recently, single image super-resolution (SR) under large scaling factors has witnessed impressive progress by introducing pre-trained generative adversarial networks (GANs) as priors. However, most GAN-Priors based SR methods are constrained by an attribute disentanglement problem in inverted latent codes which directly leads to mismatches of visual attributes in the generator layers and further degraded reconstruction. In addition, stochastic noises fed to the generator are employed for unconditional detail generation, which tends to produce unfaithful details that compromise the fidelity of the generated SR image. We design LAREN, a LAtent multi-Relation rEasoNing technique that achieves superb large-factor SR through graph-based multi-relation reasoning in latent space. LAREN consists of two innovative designs. The first is graph-based disentanglement that constructs a superior disentangled latent space via hierarchical multi-relation reasoning. The second is graph-based code generation that produces image-specific codes progressively via recursive relation reasoning which enables prior GANs to generate desirable image details. Extensive experiments show that LAREN achieves superior large-factor image SR and outperforms the state-of-the-art consistently across multiple benchmarks.
翻译:最近,在大幅缩放因素下,单个图像超分辨率(SR)在大规模缩放因素下取得了令人印象深刻的进展,引入了经过预先训练的基因对抗网络(GANs),然而,大多数基于GAN-Priors的SSR方法都受到反向潜伏代码的属性分解问题的限制,这些问题直接导致发电机层的视觉属性不匹配和进一步的退化重建。此外,向发电机输入的随机噪声用于无条件的详细生成,往往产生不真实的细节,损害生成的SR图像的忠实性。我们设计了LAREN,这是一种LAREN,一种通过潜在空间基于图形的多重关系推理实现超大功能SR的技术。LAREN由两种创新设计组成。第一种是基于图形的分解,通过分级多关系推理法,构建一个高度分解的潜伏层空间。第二种是基于图形的代码生成,通过累进式推理法,逐渐生成图像特定代码,使GANs之前的多动关系推理算,使得GANs能够通过基于图表的多重图像细节获得理想的图像。广泛的实验显示,AARNA和AA-RAREs的高级超大型图像超越了多个基准。