Image Models
Invertible Rescaling Network
Introduced by Xiao et al. in Invertible Image RescalingAn Invertible Rescaling Network (IRN) is a network for image rescaling. According to the Nyquist-Shannon sampling theorem, high-frequency contents are lost during downscaling. Ideally, we hope to keep all lost information to perfectly recover the original HR image, but storing or transferring the high-frequency information is unacceptable. In order to well address this challenge, the Invertible Rescaling Net (IRN) captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Given an HR image $x$, IRN not only downscales it into a LR image y, but also embeds the case-specific high-frequency content into an auxiliary case-agnostic latent variable $z$, whose marginal distribution obeys a fixed pre-specified distribution (e.g., isotropic Gaussian). Based on this model, we use a randomly drawn sample of $z$ from the pre-specified distribution for the inverse upscaling procedure, which holds the most information that one could have in upscaling.
Source: Invertible Image Rescaling
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Super-Resolution | 3 | 37.50% |
| Super-Resolution | 3 | 37.50% |
| Recommendation Systems | 1 | 12.50% |
| Image Restoration | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
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|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
