  High-resolution digital images are usually downscaled to fit various display
screens or save the cost of storage and bandwidth, meanwhile the post-upscaling
is adpoted to recover the original resolutions or the details in the zoom-in
images. However, typical image downscaling is a non-injective mapping due to
the loss of high-frequency information, which leads to the ill-posed problem of
the inverse upscaling procedure and poses great challenges for recovering
details from the downscaled low-resolution images. Simply upscaling with image
super-resolution methods results in unsatisfactory recovering performance. In
this work, we propose to solve this problem by modeling the downscaling and
upscaling processes from a new perspective, i.e. an invertible bijective
transformation, which can largely mitigate the ill-posed nature of image
upscaling. We develop an Invertible Rescaling Net (IRN) with deliberately
designed framework and objectives to produce visually-pleasing low-resolution
images and meanwhile capture the distribution of the lost information using a
latent variable following a specified distribution in the downscaling process.
In this way, upscaling is made tractable by inversely passing a randomly-drawn
latent variable with the low-resolution image through the network. Experimental
results demonstrate the significant improvement of our model over existing
methods in terms of both quantitative and qualitative evaluations of image
upscaling reconstruction from downscaled images.
