# Guided Linear Upsampling

arXiv: https://arxiv.org/abs/2307.09582 · HTML: https://mlsanity.com/paper/2307.09582/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 13 Jul 2023.

## Builds on (most cited first, 15 of 15)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Swapping Autoencoder for Deep Image Manipulation](https://mlsanity.com/paper/2007.00653/) (2020)
- [Deep Bilateral Learning for Real-Time Image Enhancement](https://mlsanity.com/paper/1707.02880/) (2017)
- [Real-Time High-Resolution Background Matting](https://mlsanity.com/paper/2012.07810/) (2020)
- [A Closed-form Solution to Photorealistic Image Stylization](https://mlsanity.com/paper/1802.06474/) (2018)
- [Invertible Image Rescaling](https://mlsanity.com/paper/2005.05650/) (2020)
- [Any-resolution Training for High-resolution Image Synthesis](https://mlsanity.com/paper/2204.07156/) (2022)
- [Learned Image Downscaling for Upscaling using Content Adaptive Resampler](https://mlsanity.com/paper/1907.12904/) (2019)
- [Fast End-to-End Trainable Guided Filter](https://mlsanity.com/paper/1803.05619/) (2018)
- [Learning Affinity-Aware Upsampling for Deep Image Matting](https://mlsanity.com/paper/2011.14288/) (2020)
- [Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer](https://mlsanity.com/paper/2004.10955/) (2020)
- [Side Window Filtering](https://mlsanity.com/paper/1905.07177/) (2019)
- [Guided Upsampling Network for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1807.07466/) (2018)
- [Real-time Localized Photorealistic Video Style Transfer](https://mlsanity.com/paper/2010.10056/) (2020)
- [Unsharp Mask Guided Filtering](https://mlsanity.com/paper/2106.01428/) (2021)

## Built on by (most cited first, 3 of 3)

- [Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization](https://mlsanity.com/paper/2504.03011/) (2025)
- [Upsample Anything: A Simple and Hard to Beat Baseline for Feature Upsampling](https://mlsanity.com/paper/2511.16301/) (2025)
- [Tackling Ill-posedness of Reversible Image Conversion with Well-posed Invertible Network](https://mlsanity.com/paper/2508.02111/) (2025)

## Abstract (arXiv)

Guided upsampling is an effective approach for accelerating high-resolution image processing. In this paper, we propose a simple yet effective guided upsampling method. Each pixel in the high-resolution image is represented as a linear interpolation of two low-resolution pixels, whose indices and weights are optimized to minimize the upsampling error. The downsampling can be jointly optimized in order to prevent missing small isolated regions. Our method can be derived from the color line model and local color transformations. Compared to previous methods, our method can better preserve detail effects while suppressing artifacts such as bleeding and blurring. It is efficient, easy to implement, and free of sensitive parameters. We evaluate the proposed method with a wide range of image operators, and show its advantages through quantitative and qualitative analysis. We demonstrate the advantages of our method for both interactive image editing and real-time high-resolution video processing. In particular, for interactive editing, the joint optimization can be precomputed, thus allowing for instant feedback without hardware acceleration.