# Class-Aware Fully-Convolutional Gaussian and Poisson Denoising

arXiv: https://arxiv.org/abs/1808.06562 · HTML: https://mlsanity.com/paper/1808.06562/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2018.

## Built on by (most cited first, 12 of 12)

- [Image Denoising: The Deep Learning Revolution and Beyond -- A Survey Paper --](https://mlsanity.com/paper/2301.03362/) (2023)
- [Mobile Computational Photography: A Tour](https://mlsanity.com/paper/2102.09000/) (2021)
- [Dancing under the stars: video denoising in starlight](https://mlsanity.com/paper/2204.04210/) (2022)
- [Stochastic Image Denoising by Sampling from the Posterior Distribution](https://mlsanity.com/paper/2101.09552/) (2021)
- [Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser](https://mlsanity.com/paper/2103.10234/) (2021)
- [Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network](https://mlsanity.com/paper/2105.04746/) (2021)
- [Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network](https://mlsanity.com/paper/2308.05404/) (2023)
- [Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting](https://mlsanity.com/paper/2102.12095/) (2021)
- [Weighted Anisotropic-Isotropic Total Variation for Poisson Denoising](https://mlsanity.com/paper/2307.00439/) (2023)
- [Tell Me What You See: Text-Guided Real-World Image Denoising](https://mlsanity.com/paper/2312.10191/) (2023)
- [Image Denoising using Attention-Residual Convolutional Neural Networks](https://mlsanity.com/paper/2101.07713/) (2021)
- [Building Resilience to Out-of-Distribution Visual Data via Input Optimization and Model Finetuning](https://mlsanity.com/paper/2211.16228/) (2022)

## Abstract (arXiv)

We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful. Its structure allows to exploit the gradual nature of the denoising process, in which shallow layers handle local noise statistics, while deeper layers recover edges and enhance textures. Our method advances the state-of-the-art when trained for different noise levels and distributions (both Gaussian and Poisson). In addition, we show that making the denoiser class-aware by exploiting semantic class information boosts performance, enhances textures and reduces artifacts.