# Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network

arXiv: https://arxiv.org/abs/2105.00273 · HTML: https://mlsanity.com/paper/2105.00273/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising](https://mlsanity.com/paper/1710.04026/) (2017)
- [Learning Deep CNN Denoiser Prior for Image Restoration](https://mlsanity.com/paper/1704.03264/) (2017)
- [Deep Learning on Image Denoising: An overview](https://mlsanity.com/paper/1912.13171/) (2019)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections](https://mlsanity.com/paper/1606.08921/) (2016)
- [Objective Diagnosis for Histopathological Images Based on Machine Learning Techniques: Classical Approaches and New Trends](https://mlsanity.com/paper/2011.05790/) (2020)

## Built on by (most cited first, 2 of 2)

- [Weak-signal extraction enabled by deep-neural-network denoising of diffraction data](https://mlsanity.com/paper/2209.09247/) (2022)
- [Recent Advancements in Microscopy Image Enhancement using Deep Learning: A Survey](https://mlsanity.com/paper/2509.15363/) (2025)

## Abstract (arXiv)

In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by both intrinsic noise, due to the device limitations, and, by extrinsic signal perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convolutional encoder-decoder neural network is proposed. Specifically, the encoder uses deterministic mapping to map features into a hidden representation. Then, the latent representation is rebuilt to generate the reconstructed denoised image. Residual learning strategies are used to improve and accelerate the training process using skip connections in bridging across convolutional and deconvolutional layers. The proposed model reaches on average 38.38 of PSNR and 0.98 of SSIM on a test set of 57458 images overcoming state-of-the-art models in the same application domain