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

arXiv: https://arxiv.org/abs/1708.06453 · HTML: https://mlsanity.com/paper/1708.06453/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2017.

## Built on by (most cited first, 29 of 29)

- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [CoreDiff: Contextual Error-Modulated Generalized Diffusion Model for Low-Dose CT Denoising and Generalization](https://mlsanity.com/paper/2304.01814/) (2023)
- [DU-GAN: Generative Adversarial Networks with Dual-Domain U-Net Based Discriminators for Low-Dose CT Denoising](https://mlsanity.com/paper/2108.10772/) (2021)
- [Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising](https://mlsanity.com/paper/2305.15887/) (2023)
- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
- [DenoMamba: A fused state-space model for low-dose CT denoising](https://mlsanity.com/paper/2409.13094/) (2024)
- [The Perception-Robustness Tradeoff in Deterministic Image Restoration](https://mlsanity.com/paper/2311.09253/) (2023)
- [Posterior temperature optimized Bayesian models for inverse problems in medical imaging](https://mlsanity.com/paper/2202.00986/) (2022)
- [A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud](https://mlsanity.com/paper/2310.00246/) (2023)
- [A SSIM Guided cGAN Architecture For Clinically Driven Generative Image Synthesis of Multiplexed Spatial Proteomics Channels](https://mlsanity.com/paper/2205.10373/) (2022)
- [Efficient Quantum Circuits for Machine Learning Activation Functions including Constant T-depth ReLU](https://mlsanity.com/paper/2404.06059/) (2024)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising](https://mlsanity.com/paper/2508.17299/) (2025)
- [Learned Interferometric Imaging for the SPIDER Instrument](https://mlsanity.com/paper/2301.10260/) (2023)
- [Enhanced Low-Dose CT Image Reconstruction by Domain and Task Shifting Gaussian Denoisers](https://mlsanity.com/paper/2403.03551/) (2024)
- [Iterative CT Reconstruction via Latent Variable Optimization of Shallow Diffusion Models](https://mlsanity.com/paper/2408.03156/) (2024)
- [A Denoising Framework for Real-World Ultra-Low-Dose Lung CT Images Based on an Image Purification Strategy](https://mlsanity.com/paper/2510.07492/) (2025)
- [Three-dimensional visualization of X-ray micro-CT with large-scale datasets: Efficiency and accuracy for real-time interaction](https://mlsanity.com/paper/2601.15098/) (2026)
- [Window-Level is a Strong Denoising Surrogate](https://mlsanity.com/paper/2105.07153/) (2021)
- [Diffusion Denoising for Low-Dose-CT Model](https://mlsanity.com/paper/2301.11482/) (2023)
- [A Low-dose CT Reconstruction Network Based on TV-regularized OSEM Algorithm](https://mlsanity.com/paper/2408.13832/) (2024)
- [GAN-Based Architecture for Low-dose Computed Tomography Imaging Denoising](https://mlsanity.com/paper/2411.09512/) (2024)
- [Physics-Guided Null-Space Diffusion with Sparse Masking for Corrective Sparse-View CT Reconstruction](https://mlsanity.com/paper/2509.05992/) (2025)
- [IPv2: An Improved Image Purification Strategy for Real-World Ultra-Low-Dose Lung CT Denoising](https://mlsanity.com/paper/2602.19314/) (2026)
- [PatchDenoiser: Parameter-efficient multi-scale patch learning and fusion denoiser for Low-dose CT imaging](https://mlsanity.com/paper/2602.21987/) (2026)
- [Rethinking Cross-Dose PET Denoising: Mitigating Averaging Effects via Residual Noise Learning](https://mlsanity.com/paper/2604.16925/) (2026)
- [CT-DegradBench: A Physics-Informed Benchmark for CT Degradation Detection and Severity Estimation](https://mlsanity.com/paper/2605.16431/) (2026)
- [GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization](https://mlsanity.com/paper/2607.11941/) (2026)
- [Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching](https://mlsanity.com/paper/2610.18379/) (2026)

## 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.