# An unsupervised deep learning framework for medical image denoising

arXiv: https://arxiv.org/abs/2103.06575 · HTML: https://mlsanity.com/paper/2103.06575/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)](https://mlsanity.com/paper/1702.00288/) (2017)
- [Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss](https://mlsanity.com/paper/1708.00961/) (2017)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era](https://mlsanity.com/paper/1906.06543/) (2019)
- [Back-Projection based Fidelity Term for Ill-Posed Linear Inverse Problems](https://mlsanity.com/paper/1906.06794/) (2019)
- [Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks](https://mlsanity.com/paper/1903.11431/) (2019)
- [Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI](https://mlsanity.com/paper/1302.2712/) (2013)

## Built on by (most cited first, 4 of 4)

- [Adversarial Distortion Learning for Medical Image Denoising](https://mlsanity.com/paper/2204.14100/) (2022)
- [Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning](https://mlsanity.com/paper/2402.09629/) (2024)
- [Explicit Abnormality Extraction for Unsupervised Motion Artifact Reduction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2301.01732/) (2023)
- [A Strictly Bounded Deep Network for Unpaired Cyclic Translation of Medical Images](https://mlsanity.com/paper/2311.02480/) (2023)

## Abstract (arXiv)

Medical image acquisition is often intervented by unwanted noise that corrupts the information content. This paper introduces an unsupervised medical image denoising technique that learns noise characteristics from the available images and constructs denoised images. It comprises of two blocks of data processing, viz., patch-based dictionaries that indirectly learn the noise and residual learning (RL) that directly learns the noise. The model is generalized to account for both 2D and 3D images considering different medical imaging instruments. The images are considered one-by-one from the stack of MRI/CT images as well as the entire stack is considered, and decomposed into overlapping image/volume patches. These patches are given to the patch-based dictionary learning to learn noise characteristics via sparse representation while given to the RL part to directly learn the noise properties. K-singular value decomposition (K-SVD) algorithm for sparse representation is used for training patch-based dictionaries. On the other hand, residue in the patches is trained using the proposed deep residue network. Iterating on these two parts, an optimum noise characterization for each image/volume patch is captured and in turn it is subtracted from the available respective image/volume patch. The obtained denoised image/volume patches are finally assembled to a denoised image or 3D stack. We provide an analysis of the proposed approach with other approaches. Experiments on MRI/CT datasets are run on a GPU-based supercomputer and the comparative results show that the proposed algorithm preserves the critical information in the images as well as improves the visual quality of the images.