基于线性注意力的深度非局部均值滤波用于乘性噪声去除
Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal
AI总结：
本文提出基于深度学习的线性注意力机制的非局部均值滤波方法，用于高效去除乘性噪声，通过深度通道卷积神经网络提取邻域矩阵信息，并利用注意力机制实现相似性计算和加权平均，实验表明其在模拟和真实噪声数据上表现优于现有方法。
摘要：
乘性噪声广泛存在于雷达图像、医学图像等重要领域。与普通噪声相比，乘性噪声对图像的视觉表达影响更显著。针对乘性噪声去噪问题，本文通过深度学习线性化非局部均值算法，提出基于线性注意力机制的深度非局部均值滤波（LDNLM）。从传统非局部均值滤波出发，利用深度通道卷积神经网络提取邻域矩阵信息，获得每个像素的表示向量。然后用注意力机制的内操作替代相似性计算和加权平均过程。为减少计算开销，通过相似性计算和加权平均的公式推导出具有线性复杂度的非局部滤波器。在模拟和真实乘性噪声上的实验表明，LDNLM比现有方法更具竞争力。此外，我们证明LDNLM的可解释性接近传统NLM。源代码和预训练模型可在https://github.com/ShowiBin/LDNLM获取。
英文摘要：
Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicative noise has a generally stronger effect on the visual expression of images. Aiming at the denoising problem of multiplicative noise, we linearize the nonlocal means algorithm with deep learning and propose a linear attention mechanism based deep nonlocal means filtering (LDNLM). Starting from the traditional nonlocal means filtering, we employ deep channel convolution neural networks to extract the information of the neighborhood matrix and obtain representation vectors of every pixel. Then we replace the similarity calculation and weighted averaging processes with the inner operations of the attention mechanism. To reduce the computational overhead, through the formula of similarity calculation and weighted averaging, we derive a nonlocal filter with linear complexity. Experiments on both simulated and real multiplicative noise demonstrate that the LDNLM is more competitive compared with the state-of-the-art methods. Additionally, we prove that the LDNLM possesses interpretability close to traditional NLM. The source code and pre-trained model are available at https://github.com/ShowiBin/LDNLM.
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。
