mlsanityData to 7 Oct 2026

Wasserstein Patch Prior for Image Superresolution

arXiv 2109.12880 ↗First posted 27 Sep 2021cs.CV cs.NA math.NAcited by 13

Not on the map. Why?

Abstract (arXiv)

In this paper, we introduce a Wasserstein patch prior for superresolution of two- and three-dimensional images. Here, we assume that we have given (additionally to the low resolution observation) a reference image which has a similar patch distribution as the ground truth of the reconstruction. This assumption is e.g. fulfilled when working with texture images or material data. Then, the proposed regularizer penalizes the W2-distance of the patch distribution of the reconstruction to the patch distribution of some reference image at different scales. We demonstrate the performance of the proposed regularizer by two- and three-dimensional numerical examples.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 23 of its 67 references that are in the map. First posted on arXiv 27 Sep 2021.

Builds on

23 of 67 references.

References · 23

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2021Image Super-Resolution via Iterative Refinementcited by 1,506
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,352
  4. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  5. 2017Deep Image Priorcited by 1,097
  6. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  7. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 467
  8. 2019Deep Learning for Image Super-resolution: A Surveycited by 368
  9. 2017"Zero-Shot" Super-Resolution using Deep Internal Learningcited by 285
  10. 2016Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applicationscited by 244
  11. 2016Stochastic Optimization for Large-scale Optimal Transportcited by 188
  12. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
11 more
  1. 2017Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problemscited by 107
  2. 2015Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolationcited by 78
  3. 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 75
  4. 2018Deep Bayesian Inversioncited by 60
  5. 2017Plug-and-Play Unplugged: Optimization Free Reconstruction using Consensus Equilibriumcited by 58
  6. 2020Convolutional Proximal Neural Networks and Plug-and-Play Algorithmscited by 36
  7. 2021Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)cited by 33
  8. 2016RAISR: Rapid and Accurate Image Super Resolutioncited by 27
  9. 2020PCA Reduced Gaussian Mixture Models with Applications in Superresolutioncited by 12
  10. 20203DSNet: Unsupervised Shape-to-Shape 3D Style Transfercited by 9
  11. 2017Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100× speed-upcited by 5

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 3; 2025: 4.

13 papers.

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  1. 2021Generalized Normalizing Flows via Markov Chainscited by 23
  2. 2022PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularizationcited by 22
  3. 2023Manifold Learning by Mixture Models of VAEs for Inverse Problemscited by 16
  4. 2022WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolutioncited by 10
  5. 2023Learning from small data sets: Patch-based regularizers in inverse problems for image reconstructioncited by 9
  6. 2024T-FAKE: Synthesizing Thermal Images for Facial Landmarkingcited by 7
  7. 2025A User's Guide to Sampling Strategies for Sliced Optimal Transportcited by 7
  8. 2024Stability of Data-Dependent Ridge-Regularization for Inverse Problemscited by 5
  9. 2025Slicing Wasserstein Over Wasserstein Via Functional Optimal Transportcited by 5
  10. 2022Training Adaptive Reconstruction Networks for Blind Inverse Problemscited by 3
  11. 2024LoFi: Neural Local Fields for Scalable Image Reconstructioncited by 2
  12. 2025Joint Manifold Learning and Optimal Transport for Dynamic Imagingcited by 1
1 more
  1. 2025Estimating Dataset Dimension via Singular Metrics under the Manifold Hypothesis: Application to Inverse Problemscited by 1