mlsanityData to 7 Oct 2026

Guided Linear Upsampling

arXiv 2307.09582 ↗First posted 13 Jul 2023cs.CV cs.GRcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 13 Jul 2023.

Builds on

15 of 53 references.

References · 15

  1. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
  2. 2020Swapping Autoencoder for Deep Image Manipulationcited by 215
  3. 2017Deep Bilateral Learning for Real-Time Image Enhancementcited by 200
  4. 2020Real-Time High-Resolution Background Mattingcited by 172
  5. 2018A Closed-form Solution to Photorealistic Image Stylizationcited by 133
  6. 2020Invertible Image Rescalingcited by 92
  7. 2022Any-resolution Training for High-resolution Image Synthesiscited by 72
  8. 2019Learned Image Downscaling for Upscaling using Content Adaptive Resamplercited by 55
  9. 2018Fast End-to-End Trainable Guided Filtercited by 50
  10. 2020Learning Affinity-Aware Upsampling for Deep Image Mattingcited by 37
  11. 2020Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfercited by 31
  12. 2019Side Window Filteringcited by 27
3 more
  1. 2018Guided Upsampling Network for Real-Time Semantic Segmentationcited by 24
  2. 2020Real-time Localized Photorealistic Video Style Transfercited by 24
  3. 2021Unsharp Mask Guided Filteringcited by 12

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Citing papers per year: 2025: 3.

3 papers.