# Boltzmann Machines and Denoising Autoencoders for Image Denoising

arXiv: https://arxiv.org/abs/1301.3468 · HTML: https://mlsanity.com/paper/1301.3468/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2013.

## Built on by (most cited first, 3 of 3)

- [Quantum Image Denoising: A Framework via Boltzmann Machines, QUBO, and Quantum Annealing](https://mlsanity.com/paper/2307.06542/) (2023)
- [Speckle Noise Reduction in Ultrasound Images using Denoising Auto-encoder with Skip Connection](https://mlsanity.com/paper/2403.02750/) (2024)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)

## Abstract (arXiv)

Image denoising based on a probabilistic model of local image patches has been employed by various researchers, and recently a deep (denoising) autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as a good model for this. In this paper, we propose that another popular family of models in the field of deep learning, called Boltzmann machines, can perform image denoising as well as, or in certain cases of high level of noise, better than denoising autoencoders. We empirically evaluate the two models on three different sets of images with different types and levels of noise. Throughout the experiments we also examine the effect of the depth of the models. The experiments confirmed our claim and revealed that the performance can be improved by adding more hidden layers, especially when the level of noise is high.