# Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence

arXiv: https://arxiv.org/abs/2203.00911 · HTML: https://mlsanity.com/paper/2203.00911/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 32 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Component Divide-and-Conquer for Real-World Image Super-Resolution](https://mlsanity.com/paper/2008.01928/) (2020)
- [Analyzing Inverse Problems with Invertible Neural Networks](https://mlsanity.com/paper/1808.04730/) (2018)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Meta-SR: A Magnification-Arbitrary Network for Super-Resolution](https://mlsanity.com/paper/1903.00875/) (2019)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [Learning A Single Network for Scale-Arbitrary Super-Resolution](https://mlsanity.com/paper/2004.03791/) (2020)
- [Learned Image Downscaling for Upscaling using Content Adaptive Resampler](https://mlsanity.com/paper/1907.12904/) (2019)

## Built on by (most cited first, 18 of 18)

- [Neural Degradation Representation Learning for All-In-One Image Restoration](https://mlsanity.com/paper/2310.12848/) (2023)
- [Source-Free Domain Adaptation for Real-world Image Dehazing](https://mlsanity.com/paper/2207.06644/) (2022)
- [Mutual-Guided Dynamic Network for Image Fusion](https://mlsanity.com/paper/2308.12538/) (2023)
- [Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images](https://mlsanity.com/paper/2211.10643/) (2022)
- [On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement](https://mlsanity.com/paper/2307.12027/) (2023)
- [ResVR: Joint Rescaling and Viewport Rendering of Omnidirectional Images](https://mlsanity.com/paper/2404.16825/) (2024)
- [Plug-and-Play Tri-Branch Invertible Block for Image Rescaling](https://mlsanity.com/paper/2412.13508/) (2024)
- [Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images](https://mlsanity.com/paper/2207.07317/) (2022)
- [Timestep-Aware Diffusion Model for Extreme Image Rescaling](https://mlsanity.com/paper/2408.09151/) (2024)
- [Generalized Lightness Adaptation with Channel Selective Normalization](https://mlsanity.com/paper/2308.13783/) (2023)
- [Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning](https://mlsanity.com/paper/2501.09320/) (2025)
- [Effective Invertible Arbitrary Image Rescaling](https://mlsanity.com/paper/2209.13055/) (2022)
- [Improving Multi-generation Robustness of Learned Image Compression](https://mlsanity.com/paper/2210.17039/) (2022)
- [LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation](https://mlsanity.com/paper/2407.09935/) (2024)
- [A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information](https://mlsanity.com/paper/2212.04314/) (2022)
- [Raising The Limit Of Image Rescaling Using Auxiliary Encoding](https://mlsanity.com/paper/2303.06747/) (2023)
- [JPEG Processing Neural Operator for Backward-Compatible Coding](https://mlsanity.com/paper/2507.23521/) (2025)
- [Tackling Ill-posedness of Reversible Image Conversion with Well-posed Invertible Network](https://mlsanity.com/paper/2508.02111/) (2025)

## Abstract (arXiv)

Deep learning based single image super-resolution models have been widely studied and superb results are achieved in upscaling low-resolution images with fixed scale factor and downscaling degradation kernel. To improve real world applicability of such models, there are growing interests to develop models optimized for arbitrary upscaling factors. Our proposed method is the first to treat arbitrary rescaling, both upscaling and downscaling, as one unified process. Using joint optimization of both directions, the proposed model is able to learn upscaling and downscaling simultaneously and achieve bidirectional arbitrary image rescaling. It improves the performance of current arbitrary upscaling models by a large margin while at the same time learns to maintain visual perception quality in downscaled images. The proposed model is further shown to be robust in cycle idempotence test, free of severe degradations in reconstruction accuracy when the downscaling-to-upscaling cycle is applied repetitively. This robustness is beneficial for image rescaling in the wild when this cycle could be applied to one image for multiple times. It also performs well on tests with arbitrary large scales and asymmetric scales, even when the model is not trained with such tasks. Extensive experiments are conducted to demonstrate the superior performance of our model.