mlsanityData to 14 Oct 2026

Multi-Reference Image Super-Resolution: A Posterior Fusion Approach

arXiv 2212.09988 ↗First posted 20 Dec 2022cs.CV eess.IVcited by 1

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Abstract (arXiv)

Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 20 Dec 2022.

Builds on

14 of 20 references.

References · 14

  1. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  2. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  3. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
  4. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  5. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  6. 2020Learning Texture Transformer Network for Image Super-Resolutioncited by 347
  7. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  8. 2019RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolutioncited by 145
  9. 2019Image Super-Resolution by Neural Texture Transfercited by 120
  10. 2018CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warpingcited by 91
  11. 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 69
  12. 2022Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observationscited by 20
2 more
  1. 2022Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-based Super-Resolutioncited by 19
  2. 2021Attention-based Multi-Reference Learning for Image Super-Resolutioncited by 12

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Citing papers per year: 2024: 1.

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  1. 2024MaRINeR: Enhancing Novel Views by Matching Rendered Images with Nearby Referencescited by 4