mlsanityData to 7 Oct 2026

Sharpness-aware Low dose CT denoising using conditional generative adversarial network

arXiv 1708.06453 ↗First posted 22 Aug 2017cs.CVcited by 28

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Abstract (arXiv)

Low Dose Computed Tomography (LDCT) has offered tremendous benefits in radiation restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset shows that the results of the proposed method have very small resolution loss and achieves better performance relative to the-state-of-art methods both quantitatively and visually.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 7; 2025: 3; 2026: 6.

28 papers.

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  1. 2023Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --cited by 71
  2. 2023CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalizationcited by 51
  3. 2021DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoisingcited by 46
  4. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
  5. 2022A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sourcescited by 13
  6. 2024DenoMamba: A fused state-space model for low-dose CT denoisingcited by 11
  7. 2023The Perception-Robustness Tradeoff in Deterministic Image Restorationcited by 7
  8. 2022Posterior temperature optimized Bayesian models for inverse problems in medical imagingcited by 3
  9. 2023A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloudcited by 3
  10. 2022A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channelscited by 2
  11. 2024Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLUcited by 2
  12. 2024HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesiscited by 2
16 more
  1. 2025FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoisingcited by 2
  2. 2023Learned Interferometric Imaging for the SPIDER Instrumentcited by 1
  3. 2024Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoiserscited by 1
  4. 2024Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Modelscited by 1
  5. 2025A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategycited by 1
  6. 2026Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interactioncited by 1
  7. 2021Window-Level is a Strong Denoising Surrogatecited by 0
  8. 2023Diffusion Denoising for Low-Dose-CT Modelcited by 0
  9. 2024A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithmcited by 0
  10. 2024GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoisingcited by 0
  11. 2025Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstructioncited by 0
  12. 2026IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoisingcited by 0
  13. 2026PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imagingcited by 0
  14. 2026Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learningcited by 0
  15. 2026CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimationcited by 0
  16. 2026GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalizationcited by 0