论文标题
QC-Stylegan-质量可控的图像生成和操纵
QC-StyleGAN -- Quality Controllable Image Generation and Manipulation
论文作者
论文摘要
引入高质量的图像生成模型，尤其是StyleGan家族，为合成和操纵图像提供了强大的工具。但是，现有模型是根据所需的输出建立在高质量（HQ）数据的基础上的，这使得它们不适合野外低质量（LQ）图像，这是进行操作的常见输入。在这项工作中，我们通过提出一种新颖的gan结构来弥合这一差距，该结构允许以可控质量生成图像。网络可以合成各种图像降解，并通过质量控制代码恢复锋利的图像。我们提出的QC-Stylegan可以通过应用GAN反转和操纵技术直接编辑LQ图像而不改变其质量。它还提供了一个免费的图像恢复解决方案，该解决方案可以处理各种降解，包括噪声，模糊，压缩伪像及其混合物。最后，我们演示了许多其他应用，例如图像降解合成，转移和插值。该代码可在https://github.com/vinairesearch/qc-stylegan上找到。
The introduction of high-quality image generation models, particularly the StyleGAN family, provides a powerful tool to synthesize and manipulate images. However, existing models are built upon high-quality (HQ) data as desired outputs, making them unfit for in-the-wild low-quality (LQ) images, which are common inputs for manipulation. In this work, we bridge this gap by proposing a novel GAN structure that allows for generating images with controllable quality. The network can synthesize various image degradation and restore the sharp image via a quality control code. Our proposed QC-StyleGAN can directly edit LQ images without altering their quality by applying GAN inversion and manipulation techniques. It also provides for free an image restoration solution that can handle various degradations, including noise, blur, compression artifacts, and their mixtures. Finally, we demonstrate numerous other applications such as image degradation synthesis, transfer, and interpolation. The code is available at https://github.com/VinAIResearch/QC-StyleGAN.
