Reversible image conversion (RIC) aims to build a reversible transformation
between specific visual content (e.g., short videos) and an embedding image,
where the original content can be restored from the embedding when necessary.
This work develops Invertible Image Conversion Net (IICNet) as a generic
solution to various RIC tasks due to its strong capacity and task-independent
design. Unlike previous encoder-decoder based methods, IICNet maintains a
highly invertible structure based on invertible neural networks (INNs) to
better preserve the information during conversion. We use a relation module and
a channel squeeze layer to improve the INN nonlinearity to extract cross-image
relations and the network flexibility, respectively. Experimental results
demonstrate that IICNet outperforms the specifically-designed methods on
existing RIC tasks and can generalize well to various newly-explored tasks.
With our generic IICNet, we no longer need to hand-engineer task-specific
embedding networks for rapidly occurring visual content. Our source codes are
available at: this https URL

本论文提出了可逆图像转换网络 (IICNet)，采用可逆神经网络 (INNs) 结构，内含关系模块和通道压缩层，可成为不同 Reversible image conversion 任务的通用解决方案，可提取跨图像关系的非线性性和网络的灵活性，相较于之前的方法表现更优，同时在各种新探索任务中具有良好的泛化性能，是一种有效的可逆图像转换算法。