DoPAMINE：用于像素自适应乘性噪声降斑的双侧掩蔽CNN
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling
摘要：
我们提出了一种新的基于神经网络的乘性噪声降斑算法DoPAMINE。我们的算法的灵感来自神经AIDE（N-AIDE），这是最近提出的神经自适应图像去噪。虽然原始的N-AIDE是为加性噪声情况设计的，但我们证明了相同的框架，即，通过最小化MSE的无偏估计来自适应地学习用于逐像素仿射去噪器的网络也可以应用于乘性噪声的情况。此外，我们推导出一种双面掩蔽的CNN架构，该架构可以控制每层中激活值的方差，并在监督训练期间快速收敛到高去噪性能。在实验结果中，我们表明我们的DoPAMINE具有很高的自适应性，通过微调网络参数的基础上给定的噪声图像，并取得显着更好的去斑效果相比，SAR-DRN，最先进的CNN为基础的算法。
英文摘要：
We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original N-AIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each layer and converge fast to high denoising performance during supervised training. In the experimental results, we show our DoPAMINE possesses high adaptivity via fine-tuning the network parameters based on the given noisy image and achieves significantly better despeckling results compared to SAR-DRN, a state-of-the-art CNN-based algorithm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
