论文标题
衡量标准可逆图像缩小图像
Scale-arbitrary Invertible Image Downscaling
论文作者
论文摘要
常规的社交媒体平台通常会降低人力资源图像，以将其解决方案限制为特定的尺寸，以节省传输/存储成本，从而导致超级分辨率（SR）高度不良。最近的可逆图像降尺度方法共同对降尺度/放大问题进行建模，并实现重大改进。但是，他们仅考虑固定的整数量表因素，这些因素无法通过各种决议来降低人力资源图像，以满足社交媒体平台的分辨率限制。在本文中，我们提出了一个偏低的尺度可逆图像降尺度网络（AIDN），以与具有任意尺度因子的天生降级HR图像。同时，人力资源信息以几乎不可察觉的形式嵌入到较小的低分辨率（LR）对应物中，以便我们的AIDN也可以仅从LR图像中恢复原始的HR图像。支持任意规模因素的关键是我们提出的条件重采样模块（CRM），该模块在尺度因子和图像含量上都构成了降尺度/缩放内核和采样位置的条件。广泛的实验结果表明，我们的AIDN通过任意整数和非全能量表因子均可缩放降低尺度的最高效果。代码将在出版后发布。
Conventional social media platforms usually downscale the HR images to restrict their resolution to a specific size for saving transmission/storage cost, which leads to the super-resolution (SR) being highly ill-posed. Recent invertible image downscaling methods jointly model the downscaling/upscaling problems and achieve significant improvements. However, they only consider fixed integer scale factors that cannot downscale HR images with various resolutions to meet the resolution restriction of social media platforms. In this paper, we propose a scale-Arbitrary Invertible image Downscaling Network (AIDN), to natively downscale HR images with arbitrary scale factors. Meanwhile, the HR information is embedded in the downscaled low-resolution (LR) counterparts in a nearly imperceptible form such that our AIDN can also restore the original HR images solely from the LR images. The key to supporting arbitrary scale factors is our proposed Conditional Resampling Module (CRM) that conditions the downscaling/upscaling kernels and sampling locations on both scale factors and image content. Extensive experimental results demonstrate that our AIDN achieves top performance for invertible downscaling with both arbitrary integer and non-integer scale factors. Code will be released upon publication.
