# Evaluating BM3D and NBNet: A Comprehensive Study of Image Denoising Across Multiple Datasets

arXiv: https://arxiv.org/abs/2408.05697 · HTML: https://mlsanity.com/paper/2408.05697/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 11 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [NBNet: Noise Basis Learning for Image Denoising with Subspace Projection](https://mlsanity.com/paper/2012.15028/) (2020)
- [CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition](https://mlsanity.com/paper/1712.02463/) (2017)
- [CURE-OR: Challenging Unreal and Real Environments for Object Recognition](https://mlsanity.com/paper/1810.08293/) (2018)
- [UNIQUE: Unsupervised Image Quality Estimation](https://mlsanity.com/paper/1810.06631/) (2018)
- [CSV: Image Quality Assessment Based on Color, Structure, and Visual System](https://mlsanity.com/paper/1810.06464/) (2018)
- [MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation](https://mlsanity.com/paper/1811.08947/) (2018)
- [Perceptual Image Quality Assessment through Spectral Analysis of Error Representations](https://mlsanity.com/paper/1810.05964/) (2018)

## Abstract (arXiv)

This paper investigates image denoising, comparing traditional non-learning-based techniques, represented by Block-Matching 3D (BM3D), with modern learning-based methods, exemplified by NBNet. We assess these approaches across diverse datasets, including CURE-OR, CURE-TSR, SSID+, Set-12, and Chest-Xray, each presenting unique noise challenges. Our analysis employs seven Image Quality Assessment (IQA) metrics and examines the impact on object detection performance. We find that while BM3D excels in scenarios like blur challenges, NBNet is more effective in complex noise environments such as under-exposure and over-exposure. The study reveals the strengths and limitations of each method, providing insights into the effectiveness of different denoising strategies in varied real-world applications.