Invertible Rescaling Networks (IRNs) and their variants have witnessed
remarkable achievements in various image processing tasks like image rescaling.
However, we observe that IRNs with deeper networks are difficult to train, thus
hindering the representational ability of IRNs. To address this issue, we
propose Invertible Residual Rescaling Models (IRRM) for image rescaling by
learning a bijection between a high-resolution image and its low-resolution
counterpart with a specific distribution. Specifically, we propose IRRM to
build a deep network, which contains several Residual Downscaling Modules
(RDMs) with long skip connections. Each RDM consists of several Invertible
Residual Blocks (IRBs) with short connections. In this way, RDM allows rich
low-frequency information to be bypassed by skip connections and forces models
to focus on extracting high-frequency information from the image. Extensive
experiments show that our IRRM performs significantly better than other
state-of-the-art methods with much fewer parameters and complexity.
Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over
HCFlow and IRN in the $\times 4$ rescaling while only using 60\% parameters and
50\% FLOPs. The code will be available at this https URL

通过学习特定分布下的高分辨率图像与低分辨率图像之间的双射映射，我们提出了可逆残差缩放模型 (IRRM) 来构建深度网络，通过长跳跃连接的残差降采样模块 (RDM) 和短连接的可逆残差块 (IRB) 来强制模型提取图像的高频信息，从而在图像缩放任务中显著优于其他先进方法，且使用更少的参数和复杂性。