# On the Relation between Color Image Denoising and Classification

arXiv: https://arxiv.org/abs/1704.01372 · HTML: https://mlsanity.com/paper/1704.01372/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2017.

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

- [Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather](https://mlsanity.com/paper/2110.07206/) (2021)
- [Pushing Joint Image Denoising and Classification to the Edge](https://mlsanity.com/paper/2409.08943/) (2024)

## Abstract (arXiv)

Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we investigate the interaction between denoising and classification on large scale dataset. Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery. We study the importance of (sufficient) training data, how semantic class information can be traded for improved denoising results. As a result, our method greatly improves PSNR performance by 0.34 - 0.51 dB on average over state-of-the art methods on large scale dataset. We conclude that it is beneficial to incorporate in classification models. On the other hand, we also study how noise affect classification performance. In the end, we come to a number of interesting conclusions, some being counter-intuitive.