# Deep Learning for Image Denoising: A Survey

arXiv: https://arxiv.org/abs/1810.05052 · HTML: https://mlsanity.com/paper/1810.05052/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 11 of 11)

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- [Cascading Modular Network (CAM-Net) for Multimodal Image Synthesis](https://mlsanity.com/paper/2106.09015/) (2021)
- [EraseNet: A Recurrent Residual Network for Supervised Document Cleaning](https://mlsanity.com/paper/2210.00708/) (2022)
- [RSEND: Retinex-based Squeeze and Excitation Network with Dark Region Detection for Efficient Low Light Image Enhancement](https://mlsanity.com/paper/2406.09656/) (2024)
- [Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images](https://mlsanity.com/paper/2408.12013/) (2024)

## Abstract (arXiv)

Since the proposal of big data analysis and Graphic Processing Unit (GPU), the deep learning technology has received a great deal of attention and has been widely applied in the field of imaging processing. In this paper, we have an aim to completely review and summarize the deep learning technologies for image denoising proposed in recent years. Morever, we systematically analyze the conventional machine learning methods for image denoising. Finally, we point out some research directions for the deep learning technologies in image denoising.