**arXiv ID:** 2103.03060 **Authors:** Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj **Published:** 2021-03-04T14:37:23Z **Abstract:** Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to p...
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# BM3D vs 2-Layer ONN
**arXiv ID:** 2103.03060
**Authors:** Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj
**Published:** 2021-03-04T14:37:23Z
**Abstract:**
Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to produce competitive results as compared to BM3D for AWGN image denoising. To this end, we configure networks with only two hidden layers and employ different neuron models and layer widths for comparing the performance with BM3D across different AWGN noise levels. Our results conclusively show that the recently proposed self-organized variant of operational neural networks based on a generative neuron model (Self-ONNs) is not only a better choice as compared to CNNs, but also provide competitive results as compared to BM3D and even significantly surpass it for high noise levels.
## Skill Description
This skill is generated from the arXiv paper: BM3D vs 2-Layer ONN (2103.03060).
## How to Use
[To be filled in by the user or by future automation]
## References
- [arXiv:2103.03060](http://arxiv.org/abs/2103.03060v1)
