Scale-arbitrary Invertible Image Downscaling
Jinbo Xing, Wenbo Hu, Tien-Tsin Wong

TL;DR
This paper introduces AIDN, a novel invertible image downscaling network capable of handling arbitrary scale factors, preserving high-resolution details and enabling accurate restoration of original images, thus addressing limitations of fixed-scale methods.
Contribution
The proposed AIDN is the first to support scale-arbitrary invertible image downscaling, embedding HR information in LR images for flexible resolution control.
Findings
Achieves top performance on invertible downscaling tasks.
Supports both integer and non-integer scale factors.
Maintains nearly imperceptible HR information in LR images.
Abstract
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…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Advanced Neuroimaging Techniques and Applications
