mlsanityData to 14 Oct 2026

Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss

arXiv 1708.00961 ↗First posted 3 Aug 2017cs.CVcited by 162

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we introduce a new CT image denoising method based on the generative adversarial network (GAN) with Wasserstein distance and perceptual similarity. The Wasserstein distance is a key concept of the optimal transform theory, and promises to improve the performance of the GAN. The perceptual loss compares the perceptual features of a denoised output against those of the ground truth in an established feature space, while the GAN helps migrate the data noise distribution from strong to weak. Therefore, our proposed method transfers our knowledge of visual perception to the image denoising task, is capable of not only reducing the image noise level but also keeping the critical information at the same time. Promising results have been obtained in our experiments with clinical CT images.

Key facts. Cited by 162 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 Aug 2017.

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Citing papers per year: 2021: 39; 2022: 32; 2023: 31; 2024: 26; 2025: 20; 2026: 14.

162 papers.

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  1. 2022Unsupervised Medical Image Translation with Adversarial Diffusion Modelscited by 192
  2. 2021Equivariant Imaging: Learning Beyond the Range Spacecited by 83
  3. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  4. 2022Conversion Between CT and MRI Images Using Diffusion and Score-Matching Modelscited by 69
  5. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 52
  6. 2021Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applicationscited by 47
  7. 2021DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoisingcited by 46
  8. 2022CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT Denoisingcited by 46
  9. 2023Zero-shot Medical Image Translation via Frequency-Guided Diffusion Modelscited by 40
  10. 2021High-Fidelity and Arbitrary Face Editingcited by 35
  11. 2021Eformer: Edge Enhancement based Transformer for Medical Image Denoisingcited by 30
  12. 2022Generative Modeling in Sinogram Domain for Sparse-view CT Reconstructioncited by 27
38 more
  1. 2021TED-net: Convolution-free T2T Vision Transformer-based Encoder-decoder Dilation network for Low-dose CT Denoisingcited by 21
  2. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
  3. 2022UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomographycited by 19
  4. 2022Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstructioncited by 18
  5. 2023IQAGPT: Image Quality Assessment with Vision-language and ChatGPT Modelscited by 18
  6. 2021Uncertainty-Guided Progressive GANs for Medical Image Translationcited by 16
  7. 2021MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Diseasecited by 16
  8. 2021Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoisingcited by 15
  9. 2021Meta-learning with implicit gradients in a few-shot setting for medical image segmentationcited by 15
  10. 2023ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoisingcited by 15
  11. 2022Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A surveycited by 14
  12. 2022Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imagingcited by 14
  13. 2022Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Frameworkcited by 14
  14. 20223D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesiscited by 13
  15. 2021DuDoTrans: Dual-Domain Transformer Provides More Attention for Sinogram Restoration in Sparse-View CT Reconstructioncited by 12
  16. 2023LIT-Former: Linking In-plane and Through-plane Transformers for Simultaneous CT Image Denoising and Deblurringcited by 12
  17. 2021Task-Oriented Low-Dose CT Image Denoisingcited by 11
  18. 2023A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventionscited by 11
  19. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
  20. 2021Contextual colorization and denoising for low-light ultra high resolution sequencescited by 10
  21. 2022Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in Computed Tomographycited by 9
  22. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  23. 2023Diffusion Prior Regularized Iterative Reconstruction for Low-dose CTcited by 9
  24. 2021Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolationcited by 8
  25. 2022Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Imagescited by 8
  26. 2024Do As I Do: Pose Guided Human Motion Copycited by 8
  27. 2021Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion Detectioncited by 7
  28. 2022WNet: A data-driven dual-domain denoising model for sparse-view computed tomography with a trainable reconstruction layercited by 7
  29. 2023Parallel Diffusion Model-based Sparse-view Cone-beam Breast CTcited by 7
  30. 2024Low-dose CT Denoising with Language-engaged Dual-space Alignmentcited by 7
  31. 2025Lightweight Medical Image Restoration via Integrating Reliable Lesion-Semantic Driven Priorcited by 7
  32. 2021Provably Convergent Learned Inexact Descent Algorithm for Low-Dose CT Reconstructioncited by 6
  33. 2021Deep neural networks-based denoising models for CT imaging and their efficacycited by 6
  34. 2023A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoisingcited by 6
  35. 2023Latent Diffusion Model for Medical Image Standardization and Enhancementcited by 6
  36. 2023Domain Generalization of 3D Object Detection by Density-Resamplingcited by 6
  37. 2024GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesiscited by 6
  38. 2024Imaging foundation model for universal enhancement of non-ideal measurement CTcited by 6

50 most cited of 162 All 162 (JSON)