mlsanityData to 7 Oct 2026

Medical image denoising using convolutional denoising autoencoders

arXiv 1608.04667 ↗First posted 16 Aug 2016cs.CV stat.MLcited by 76

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Abstract (arXiv)

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Aug 2016.

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Citing papers per year: 2021: 19; 2022: 19; 2023: 10; 2024: 12; 2025: 14; 2026: 2.

76 papers.

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  1. 2022Learning two-phase microstructure evolution using neural operators and autoencoder architecturescited by 72
  2. 2021Eformer: Edge Enhancement based Transformer for Medical Image Denoisingcited by 30
  3. 2021SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRIcited by 16
  4. 2022Towards Adversarially Robust Deep Image Denoisingcited by 13
  5. 2021Revisiting Bayesian Autoencoders with MCMCcited by 10
  6. 2022Direct data-driven forecast of local turbulent heat flux in Rayleigh-B'enard convectioncited by 10
  7. 2022Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcomacited by 9
  8. 2022Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentationcited by 8
  9. 2021MalPhase: Fine-Grained Malware Detection Using Network Flow Datacited by 7
  10. 2021A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methodscited by 7
  11. 2022ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networkscited by 7
  12. 2023Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attackcited by 6
38 more
  1. 2024TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancementcited by 6
  2. 2022Reducing Redundancy in the Bottleneck Representation of the Autoencoderscited by 5
  3. 2022JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imagingcited by 5
  4. 2024Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAscited by 5
  5. 2024LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencodercited by 5
  6. 2021Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Seriescited by 4
  7. 2021MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoderscited by 4
  8. 2022Adversarial Distortion Learning for Medical Image Denoisingcited by 4
  9. 2022DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformationcited by 4
  10. 20223D Brain and Heart Volume Generative Models: A Surveycited by 4
  11. 2022Fantomas: Understanding Face Anonymization Reversibilitycited by 4
  12. 2023Exploring Adversarial Attacks on Neural Networks: An Explainable Approachcited by 4
  13. 2025Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challengecited by 4
  14. 2021Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networkscited by 3
  15. 2023Information-Ordered Bottlenecks for Adaptive Semantic Compressioncited by 3
  16. 2023D3A-TS: Denoising-Driven Data Augmentation in Time Seriescited by 3
  17. 2024Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesiscited by 3
  18. 2024TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problemscited by 3
  19. 2021Blind microscopy image denoising with a deep residual and multiscale encoder/decoder networkcited by 2
  20. 2021Learning Personal Representations from fMRIby Predicting Neurofeedback Performancecited by 2
  21. 2022R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classificationcited by 2
  22. 2023Improving the Timing Resolution of Positron Emission Tomography Detectors Using Boosted Learning -- A Residual Physics Approachcited by 2
  23. 2023NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attackscited by 2
  24. 2024KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learningcited by 2
  25. 2025Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Imagescited by 2
  26. 20213D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutionscited by 1
  27. 2021Predicting the Success of Domain Adaptation in Text Similaritycited by 1
  28. 2021FMNet: Latent Feature-wise Mapping Network for Cleaning up Noisy Micro-Doppler Spectrogramcited by 1
  29. 2021Neuronal Learning Analysis using Cycle-Consistent Adversarial Networkscited by 1
  30. 2023Deep Learning in Healthcare: An In-Depth Analysiscited by 1
  31. 2023GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configurationcited by 1
  32. 2024Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connectioncited by 1
  33. 2024DCAE-SR: Design of a Denoising Convolutional Autoencoder for reconstructing Electrocardiograms signals at Super Resolutioncited by 1
  34. 2024Ultrasound Image Enhancement with the Variance of Diffusion Modelscited by 1
  35. 2025Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methodscited by 1
  36. 2025Deep Perceptual Enhancement for Medical Image Analysiscited by 1
  37. 2025DONUT: Physics-aware Machine Learning for Real-time X-ray Nanodiffraction Analysiscited by 1
  38. 2026Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modelingcited by 1

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