# Medical image denoising using convolutional denoising autoencoders

arXiv: https://arxiv.org/abs/1608.04667 · HTML: https://mlsanity.com/paper/1608.04667/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2016.

## Built on by (most cited first, 30 of 76)

- [Learning two-phase microstructure evolution using neural operators and autoencoder architectures](https://mlsanity.com/paper/2204.07230/) (2022)
- [Eformer: Edge Enhancement based Transformer for Medical Image Denoising](https://mlsanity.com/paper/2109.08044/) (2021)
- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [Towards Adversarially Robust Deep Image Denoising](https://mlsanity.com/paper/2201.04397/) (2022)
- [Revisiting Bayesian Autoencoders with MCMC](https://mlsanity.com/paper/2104.05915/) (2021)
- [Direct data-driven forecast of local turbulent heat flux in Rayleigh-B'enard convection](https://mlsanity.com/paper/2202.13129/) (2022)
- [Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma](https://mlsanity.com/paper/2204.13838/) (2022)
- [Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation](https://mlsanity.com/paper/2202.09798/) (2022)
- [MalPhase: Fine-Grained Malware Detection Using Network Flow Data](https://mlsanity.com/paper/2106.00541/) (2021)
- [A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods](https://mlsanity.com/paper/2111.01604/) (2021)
- [ECG Artifact Removal from Single-Channel Surface EMG Using Fully Convolutional Networks](https://mlsanity.com/paper/2210.13271/) (2022)
- [Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack](https://mlsanity.com/paper/2306.16050/) (2023)
- [TrustEMG-Net: Using Representation-Masking Transformer with U-Net for Surface Electromyography Enhancement](https://mlsanity.com/paper/2410.03843/) (2024)
- [Reducing Redundancy in the Bottleneck Representation of the Autoencoders](https://mlsanity.com/paper/2202.04629/) (2022)
- [JR2net: A Joint Non-Linear Representation and Recovery Network for Compressive Spectral Imaging](https://mlsanity.com/paper/2205.07770/) (2022)
- [Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs](https://mlsanity.com/paper/2401.10724/) (2024)
- [LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder](https://mlsanity.com/paper/2409.14622/) (2024)
- [Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge](https://mlsanity.com/paper/2505.02784/) (2025)
- [Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series](https://mlsanity.com/paper/2101.03850/) (2021)
- [MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoders](https://mlsanity.com/paper/2106.11914/) (2021)
- [Adversarial Distortion Learning for Medical Image Denoising](https://mlsanity.com/paper/2204.14100/) (2022)
- [DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation](https://mlsanity.com/paper/2207.08980/) (2022)
- [3D Brain and Heart Volume Generative Models: A Survey](https://mlsanity.com/paper/2210.05952/) (2022)
- [Fantomas: Understanding Face Anonymization Reversibility](https://mlsanity.com/paper/2210.10651/) (2022)
- [Exploring Adversarial Attacks on Neural Networks: An Explainable Approach](https://mlsanity.com/paper/2303.06032/) (2023)
- [Rotor Localization and Phase Mapping of Cardiac Excitation Waves using Deep Neural Networks](https://mlsanity.com/paper/2109.10472/) (2021)
- [Information-Ordered Bottlenecks for Adaptive Semantic Compression](https://mlsanity.com/paper/2305.11213/) (2023)
- [D3A-TS: Denoising-Driven Data Augmentation in Time Series](https://mlsanity.com/paper/2312.05550/) (2023)
- [Universal Topology Refinement for Medical Image Segmentation with Polynomial Feature Synthesis](https://mlsanity.com/paper/2409.09796/) (2024)
- [TAEN: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems](https://mlsanity.com/paper/2412.07010/) (2024)

## 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.