Invertible Rescaling Network and Its Extensions
- URL: http://arxiv.org/abs/2210.04188v1
- Date: Sun, 9 Oct 2022 06:58:58 GMT
- Title: Invertible Rescaling Network and Its Extensions
- Authors: Mingqing Xiao, Shuxin Zheng, Chang Liu, Zhouchen Lin, Tie-Yan Liu
- Abstract summary: In this work, we propose a novel invertible framework to model the bidirectional degradation and restoration from a new perspective.
We develop invertible models to generate valid degraded images and transform the distribution of lost contents.
Then restoration is made tractable by applying the inverse transformation on the generated degraded image together with a randomly-drawn latent variable.
- Score: 118.72015270085535
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Image rescaling is a commonly used bidirectional operation, which first
downscales high-resolution images to fit various display screens or to be
storage- and bandwidth-friendly, and afterward upscales the corresponding
low-resolution images to recover the original resolution or the details in the
zoom-in images. However, the non-injective downscaling mapping discards
high-frequency contents, leading to the ill-posed problem for the inverse
restoration task. This can be abstracted as a general image
degradation-restoration problem with information loss. In this work, we propose
a novel invertible framework to handle this general problem, which models the
bidirectional degradation and restoration from a new perspective, i.e.
invertible bijective transformation. The invertibility enables the framework to
model the information loss of pre-degradation in the form of distribution,
which could mitigate the ill-posed problem during post-restoration. To be
specific, we develop invertible models to generate valid degraded images and
meanwhile transform the distribution of lost contents to the fixed distribution
of a latent variable during the forward degradation. Then restoration is made
tractable by applying the inverse transformation on the generated degraded
image together with a randomly-drawn latent variable. We start from image
rescaling and instantiate the model as Invertible Rescaling Network (IRN),
which can be easily extended to the similar decolorization-colorization task.
We further propose to combine the invertible framework with existing
degradation methods such as image compression for wider applications.
Experimental results demonstrate the significant improvement of our model over
existing methods in terms of both quantitative and qualitative evaluations of
upscaling and colorizing reconstruction from downscaled and decolorized images,
and rate-distortion of image compression.
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