尺度任意可逆图像降尺度
Scale-arbitrary Invertible Image Downscaling
摘要：
传统的社交媒体平台通常将HR图像缩小以将其分辨率限制到特定大小以节省传输/存储成本，这导致超分辨率（SR）是高度不适定的。最近的可逆图像降尺度方法联合模型的降尺度/升尺度的问题，并取得显着的改善。然而，他们只考虑固定的整数比例因子，无法缩小具有各种分辨率的HR图像以满足社交媒体平台的分辨率限制。在本文中，我们提出了一个尺度-任意可逆图像降尺度网络（AIDN），本机降尺度的HR图像与任意比例因子。同时，HR信息以几乎不可感知的形式嵌入到缩减的低分辨率（LR）对应物中，使得我们的AIDN也可以仅从LR图像恢复原始HR图像。支持任意比例因子的关键是我们提出的条件恢复模块（CRM），它根据比例因子和图像内容来调节缩小/放大内核和采样位置。大量的实验结果表明，我们的AIDN实现可逆的降尺度与任意整数和非整数比例因子的最佳性能。代码将在发布时发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
