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BM3D vs 2-Layer ONN
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2021-03-04 , DOI: arxiv-2103.03060
Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj

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.

中文翻译:

BM3D和2层ONN

尽管最近在图像降噪方面取得了成功,但对深度和复杂体系结构的需求仍然阻碍了CNN的实际使用。较旧但计算效率更高的方法(例如BM3D)仍然是一个流行的选择,尤其是在资源受限的情况下。在这项研究中,我们旨在找出与用于WGN图像去噪的BM3D相比,紧凑型神经网络是否可以学习产生竞争性结果。为此,我们将网络配置为仅具有两个隐藏层,并使用不同的神经元模型和层宽度来比较BM3D在不同AWGN噪声水平下的性能。我们的结果最终表明,与CNN相比,最近基于生成神经元模型(Self-ONNs)提出的自组织形式的操作神经网络变体不仅是更好的选择,
更新日期:2021-03-05
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