mlsanityData to 7 Oct 2026

Variational AutoEncoder for Reference based Image Super-Resolution

arXiv 2106.04090 ↗First posted 8 Jun 2021cs.CV eess.IVcited by 18

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Abstract (arXiv)

In this paper, we propose a novel reference based image super-resolution approach via Variational AutoEncoder (RefVAE). Existing state-of-the-art methods mainly focus on single image super-resolution which cannot perform well on large upsampling factors, e.g., 8×. We propose a reference based image super-resolution, for which any arbitrary image can act as a reference for super-resolution. Even using random map or low-resolution image itself, the proposed RefVAE can transfer the knowledge from the reference to the super-resolved images. Depending upon different references, the proposed method can generate different versions of super-resolved images from a hidden super-resolution space. Besides using different datasets for some standard evaluations with PSNR and SSIM, we also took part in the NTIRE2021 SR Space challenge and have provided results of the randomness evaluation of our approach. Compared to other state-of-the-art methods, our approach achieves higher diverse scores.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 26 of its 44 references that are in the map. First posted on arXiv 8 Jun 2021.

Builds on

26 of 44 references.

References · 26

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  6. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  7. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  8. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  9. 2017High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANscited by 1,176
  10. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  11. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  12. 2017The Perception-Distortion Tradeoffcited by 582
14 more
  1. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 452
  2. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
  3. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  4. 2020SRFlow: Learning the Super-Resolution Space with Normalizing Flowcited by 209
  5. 2018Arbitrary Style Transfer with Style-Attentional Networkscited by 197
  6. 2019Image Super-Resolution by Neural Texture Transfercited by 119
  7. 2018CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warpingcited by 90
  8. 2019Explorable Super Resolutioncited by 44
  9. 2017Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Modelscited by 39
  10. 2020ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Networkcited by 27
  11. 2020DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolutioncited by 17
  12. 2019Image Super-Resolution via Attention based Back Projection Networkscited by 16
  13. 2020Unsupervised Real Image Super-Resolution via Generative Variational AutoEncodercited by 16
  14. 2019Hierarchical Back Projection Network for Image Super-Resolutioncited by 12

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Citing papers per year: 2022: 5; 2023: 4; 2024: 6; 2025: 3.

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  1. 2023Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformerscited by 35
  2. 2024Separable Multi-Concept Erasure from Diffusion Modelscited by 25
  3. 2022Reasons for the Superiority of Stochastic Estimators over Deterministic Ones: Robustness, Consistency and Perceptual Qualitycited by 13
  4. 2022How do Variational Autoencoders Learn? Insights from Representational Similaritycited by 10
  5. 2024Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolutioncited by 5
  6. 2025Deep Spatio-Temporal Neural Network for Air Quality Reanalysiscited by 4
  7. 2023How good are variational autoencoders at transfer learning?cited by 3
  8. 2024Stochasticity-aware No-Reference Point Cloud Quality Assessmentcited by 3
  9. 2022Laplacian Pyramid-like Autoencodercited by 2
  10. 2025Leveraging Vision-Language Models to Select Trustworthy Super-Resolution Samples Generated by Diffusion Modelscited by 2
  11. 2025A Comprehensive Review of Diffusion Models in Smart Agriculture: Progress, Applications, and Challengescited by 2
  12. 2022Super-resolution Reconstruction of Single Image for Latent featurescited by 1
6 more
  1. 2024Trustworthy SR: Resolving Ambiguity in Image Super-resolution via Diffusion Models and Human Feedbackcited by 1
  2. 2022β-Multivariational Autoencoder for Entangled Representation Learning in Video Framescited by 0
  3. 2023On the Adversarial Robustness of Generative Autoencoders in the Latent Spacecited by 0
  4. 2023Domain Transfer in Latent Space (DTLS) Wins on Image Super-Resolution -- a Non-Denoising Modelcited by 0
  5. 2024VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generationcited by 0
  6. 2024How to Re-enable PDE Loss for Physical Systems Modeling Under Partial Observationcited by 0